Robot Fostering Paradigms

• Robot fostering is an emerging construct in robotics and HCI that integrates structured domestic deployments, mediation of human relationships, and developmental collaboration.
• It employs curriculum-based trajectory collection and memory-enhanced architectures alongside governance mechanisms to support sustained, co-adaptive interactions.
• The paradigm emphasizes long-term, bidirectional, and regulated interactions that build trust, extend robot learning, and foster social connectivity.

Searching arXiv for papers on “Robot Fostering” and closely related usage. Robot fostering is an emerging, polysemous construct in robotics and HCI/CSCW that denotes several related but distinct programs of research. In one usage, it refers to a domestic deployment model in which households temporarily host robots under a structured curriculum to generate long-term, curated interaction trajectories (Pablo-Marti et al., 28 Sep 2025). In another, it refers to robot designs that foster human-human connection by mediating family communication, empathy, or parent-child learning rather than prioritizing a dyadic human-robot bond (Du et al., 9 Dec 2025, Shen et al., 31 Jan 2025, Ho et al., 2024). Adjacent work uses the term more broadly for mechanisms that foster trustworthy interaction, communicable learning, cooperation, and persistent robot competence across time and tasks (Girgin et al., 2024, Pusceddu et al., 29 Sep 2025, Habibian et al., 2023, Ali et al., 2024). This suggests that robot fostering is best understood as a family of relational, developmental, and governance-oriented paradigms rather than a single standardized framework.

1. Conceptual range and core commitments

Current arXiv literature uses robot fostering in at least four senses. First, it can denote a deployment-and-data-collection model in which foster households provide ecologically valid, staged, and governed domestic exposure for robots (Pablo-Marti et al., 28 Sep 2025). Second, it can denote robots fostering human-human relationships, as in robotic pets designed to support family connection and mental health among older adults, or social robots designed as “social proxies” that mediate empathy toward other people through stories (Du et al., 9 Dec 2025, Shen et al., 31 Jan 2025). Third, it can denote robots fostering human development through collaboration, especially when the robot augments parental activity rather than substituting for it in child learning (Ho et al., 2024). Fourth, it can denote mechanisms that foster trust, cooperation, and persistent capability, including communicable robot learning, bidirectional navigation interfaces, group cooperation studies, and memory-enabled cross-task action generation (Habibian et al., 2023, Girgin et al., 2024, Pusceddu et al., 29 Sep 2025, Ali et al., 2024).

Across these usages, several commitments recur. The first is a shift away from one-shot interaction toward longitudinal, co-adaptive interaction. The second is a shift away from robots as isolated autonomous agents toward robots embedded in relational ecologies involving families, caregivers, bystanders, teachers, or mixed groups. The third is an emphasis on legibility, governance, and structured exposure rather than raw interaction volume alone. A plausible implication is that robot fostering names a broader design stance: robots and humans are cultivated together through sustained, interpretable, and normatively constrained interaction.

2. Domestic robot fostering as curriculum-based trajectory collection

The most explicit formalization appears in "Fostering Robots: A Governance-First Conceptual Framework for Domestic, Curriculum-Based Trajectory Collection" (Pablo-Marti et al., 28 Sep 2025). There, Robot Fostering is a proposed domestic robot deployment model in which households temporarily host robots under a structured curriculum of real-world exposure. The paper makes the analogy to guide-dog puppy raising: households do not merely use the robot, but act as curated foster environments producing long, richly annotated, ecologically valid interaction trajectories. The central claim is not that fostering is already proven superior, but that domestic embodied AI may benefit more from carefully staged, long, richly annotated trajectories than from many short, unstructured interactions.

The curriculum is divided into three phases. Play includes single-room navigation, soft obstacles, and basic interaction and movement. Family includes multi-room patrols, portals, elevators, variable illumination, and more realistic household complexity. School includes small household projects, tool-enabled actions, and higher-level task execution. Each phase includes checklists, environmental variability, error-inducing events, and safety gates before progression. The intended trajectories include task decomposition, error recovery, social navigation, tool use, and metacognitive reflection (Pablo-Marti et al., 28 Sep 2025).

The paper formalizes an embodied trajectory as

$\tau = \{(s_t, a_t, o_t, \alpha_t)\}_{t=1}^{T}$

where $s_t$ is state, $a_t$ action, $o_t$ observation, and $\alpha_t$ annotations such as plan steps, errors, replans, and reflections. It then defines trajectory informational richness as

$$\mathcal{R}(\tau)= I(A;O\,|\,S) + H(\text{rare events}) + \lambda\,\mathrm{Delib}(\alpha)$$

where $I(A;O \mid S)$ captures controllability or action-observation informativeness conditioned on state, $H(\text{rare events})$ captures low-frequency phenomena such as glare, occlusions, and pet interference, and $\mathrm{Delib}(\alpha)$ captures the density of metacognitive markers in annotations. Practical proxies include MDL / compressibility, kNN novelty over embeddings, and rate of error–recovery segments (Pablo-Marti et al., 28 Sep 2025).

The framework also states an explicit conjecture: for a fixed token budget, curricula that maximize $\sum \mathcal{R}(\tau)$ over long trajectories yield higher generalization on hold-out homes than i.i.d. short snippets. This is presented as falsifiable rather than established. In context, the proposal reframes domestic deployment from a mere evaluation setting into an information-gathering policy under governance constraints. The household becomes part of the training ecology, but only under explicit privacy, safety, and auditability requirements.

3. Robots as mediators of human-human connection

A second major line of work treats robots not as endpoints of attachment but as mediators of interpersonal connection. "Mediating Personal Relationships with Robotic Pets for Fostering Human-Human Interaction of Older Adults" (Du et al., 9 Dec 2025) explicitly shifts the framing of robotic pets for older adults away from direct companionship and toward mediation of family, friend, and caregiver relationships. The study is an ethnographic study using one-on-one semi-structured Zoom interviews with six German older adults—five male and one female—with interviews lasting 40 minutes to 1.5 hours. Analysis followed the five phases of qualitative text analysis proposed by Kuckartz (2019), with MAXQDA used to support coding and analysis (Du et al., 9 Dec 2025).

Its strongest finding is that participants imagined robotic pets as telerobots or communication mediators. Family communication remained central, but the salient interaction patterns were telepresence and remote touch: participants described feeling that the pet could embody a son or grandson when remotely controlled, or convey affection through remote touch-like gestures. Participants also saw robotic pets as potentially useful for older adults less comfortable with smartphones or video calls, and as practical support systems for messaging, reminders, waking, household chores, and mobility-related help. The paper therefore broadens the robot’s role from companion to what it explicitly characterizes as relationship infrastructure (Du et al., 9 Dec 2025).

The design patterns identified in that work are consistent and concrete: support remote embodiment and telepresence; make the robot emotionally expressive and socially legible through voice and movement; enable gentle physicality and tactile comfort; avoid substitution by augmenting rather than replacing human ties; and support sharing and multi-user involvement. Its mental-health claim is intentionally limited: reducing loneliness, depression, or improving quality of life is presented conceptually and inferentially, not as experimentally measured outcome (Du et al., 9 Dec 2025).

A related but distinct model appears in "Social Robots as Social Proxies for Fostering Connection and Empathy Towards Humanity" (Shen et al., 31 Jan 2025). This paper defines the robot as a social proxy: a physically embodied intermediary that passes along human-authored stories rather than presenting itself as the origin of those stories. The deployment used 40 robot stations in users’ homes over two weeks, with each station including a Jibo social robot, Android tablet, Intel NUC / Ubuntu computer, USB microphone, and web camera. Participants were asked to use the system for at least 6 sessions. Each session followed five phases: warm-up, story share, story receive, story reflection, and cool-down (Shen et al., 31 Jan 2025).

The paper articulates five design dimensions: physical embodiment of stories for greater emotional involvement; increasing self-disclosure via non-judging agents; long-term relational interaction; adaptive and socially contextualized conversation; and clarifying story ownership for narrative authenticity. Thematic analysis identified several mechanisms by which the robot fostered connection: identifying connections across stories, fostering connection beyond the session itself, offering diverse perspectives, prompting perspective taking, enabling judgment-free disclosure, and acting as a supportive listening presence. Supplementary survey results indicated greater increases in connection and empathy toward stories in the social proxy condition than in the social agent condition (Shen et al., 31 Jan 2025).

Taken together, these studies replace a narrow companion paradigm with a mediational one. The robot’s value lies less in being loved as a quasi-pet or quasi-person than in carrying presence, stories, touch, reflection, or reminders across social distance. This suggests a general relational principle: when story ownership and human agency remain explicit, the robot can function as an embodied channel for human-human empathy rather than a deceptive substitute for it.

4. Parent-robot collaboration and developmental fostering

In educational HRI, robot fostering appears as a model of collaborative augmentation rather than replacement. "It's Not a Replacement:" Enabling Parent-Robot Collaboration to Support In-Home Learning Experiences of Young Children" (Ho et al., 2024) studies how parents prefer to incorporate a learning companion robot into home reading sessions with children aged 3–5. The prototype used a Misty II semi-humanoid robot running on a Raspberry Pi as a technology probe. During reading, the robot could deliver verbal educational prompts, deliver verbal responses or answers, repeat its previous utterance, be triggered through AprilTags placed in books, and be activated by physical bumper buttons. The prompts focused on informal numeracy and math talk, within a broader shared storybook-reading context (Ho et al., 2024).

The study involved 10 parent-child dyads; children were 3–5 years old, with 4 girls, 6 boys, and mean age 4.3. All participating parents were mothers. Sessions were short-term in-home visits of about 70 minutes total, comprising roughly 40 minutes of reading with the robot and 30 minutes of semi-structured interview. Audio/video recordings and field notes were analyzed with reflexive thematic analysis (Ho et al., 2024).

The principal finding is that parents wanted the robot to be a collaborator, teammate, facilitator, or helper, not a substitute teacher or parental replacement. Parents wanted the robot to generate prompts, provide answers or confirmations, handle repetitive or scripted tasks, keep children engaged, and step in when the parent was busy, distracted, or unavailable. Parents simultaneously wanted to retain control over goals and values, provide personalized scaffolding, explain processes rather than merely answers, supervise interaction, and preserve bonding and conversation. Concerns clustered around privacy, content appropriateness, and balanced technology use (Ho et al., 2024).

The paper’s most compact conceptual contribution is the CAM framework: capability, availability, and motivation. Responsibility allocation should depend on relative capability between parent and robot, parent availability, and parent motivation for repetitive or demanding tasks. It also distinguishes interaction dynamics among child-robot, parent-child-robot, and parent-child-only configurations, and distinguishes four initiative structures: user-driven; user-authored, robot-driven; robot-driven with user permission; and robot-driven. The paper uses parenting policies to refer to household rules and values governing what the robot may do, say, and when it may intervene (Ho et al., 2024).

In this formulation, fostering does not mean that the robot independently teaches the child. It means that the robot helps cultivate the conditions under which learning occurs by scaffolding dialogue, sustaining engagement, and compensating for practical parental constraints while leaving educational direction under family control. This aligns with the non-substitution principle also seen in work on older adults and social proxies.

5. Persistent robot capability and memory-based fostering

A more robot-centric sense of fostering appears in "Robots Can Multitask Too: Integrating a Memory Architecture and LLMs for Enhanced Cross-Task Robot Action Generation" (Ali et al., 2024). There, fostering refers to enabling a robot to become more capable over time across changing tasks by combining LLM-based reasoning with a human-inspired memory architecture. The paper argues that LLMs alone are insufficient for long-horizon embodied behavior because robotic tasks unfold over time, may be interrupted, and require retention of task state, environment state, and action history.

The proposed architecture is dual-layered. A Level 1 Coordinator LLM serves as the reasoning backbone: it receives the task specification and available action definitions, inspects the current detected objects, decides which memory functions to invoke, combines task context with retrieved memory, and generates the next action. A Level 0 Worker LLM handles instruction following and memory processing: it parses current visual input and task prompt, builds working memory from current task and objects, maintains declarative memory from persistent logs, and returns structured outputs used by the coordinator. The design is explicitly inspired by working, declarative, and procedural memory in human cognition (Ali et al., 2024).

The memory interface is operationalized through two symbolic actions: $a_t$7 Working memory stores task reminders, task state, and task-relevant visual objects, functioning as selective attention. Declarative memory is a persistent task log, one file per task, storing executed actions, object placements, remaining objects, container contents, and task state after each step. This structure is what allows interrupted tasks to be resumed rather than restarted (Ali et al., 2024).

Evaluation covered five tabletop robotic tasks: sorting, arrangement, pointing, recipe, and tower. Available actions included <point(object)>, <give(object)>, <move_to_box_1(object)>, <move_to_box_2(object)>, <put_on_tower(object)>, and <place_in_bowl(object)>. The comparison involved standalone, consecutive, and intervened modes, with metrics of Success Rate, Task Retention, and Environment Retention. In standalone mode, performance was high for both LLMs; the key result was that performance degraded sharply without memory in consecutive and intervened modes, then recovered substantially with memory. For example, in consecutive mode, Arrangement with GPT-3.5 rose from 0.14 success without memory to 1.00 with memory, and Point with GPT-3.5 rose from 0.40 to 1.00. In intervened mode, Tower with Llama 3 rose from 0.15 without memory to 0.98 with memory (Ali et al., 2024).

The reported failure modes without memory were forgetting task specifications, acting on irrelevant objects, choosing the wrong object classes, producing redundant or noisy actions, and failing to respect task order. The paper therefore presents fostering as a cognitive scaffold for persistence: the robot becomes capable not merely of issuing plausible next actions, but of preserving task continuity across interruptions and switches. A plausible implication is that some forms of robot fostering concern not who hosts or interacts with the robot, but how the robot accumulates and reuses structured experience over time.

6. Trust, cooperation, and communicable learning

Several papers do not use robot fostering as a deployment metaphor, but study mechanisms by which robots foster trust, cohesion, and mutual intelligibility. "Bidirectional Human Interactive AI Framework for Social Robot Navigation" (Girgin et al., 2024) proposes an end-to-end pipeline for trustworthy bidirectional human-robot interaction in social navigation. The robot uses an RGB camera and 2D LiDAR; LiDAR point clouds are projected into the image plane using weak perspective projection; people are segmented with YOLACT; trajectories $s_t$0 are encoded by a pretrained LSTM into latent representations $s_t$1; and a Graph Attention Network (GAT) predicts future positions $s_t$2. These predicted positions are inserted into an occupancy-grid representation for ROS-based planning. The trust module then adds verbal explanation and gesture-based human guidance (Girgin et al., 2024).

The interaction loop is explicitly bidirectional. The robot verbally explains routes using utterances such as “I’m going straight to avoid future collusion,” and the human guides via gestures classified as wait, go left, go right, continue, and unknown. Conflict detection compares the actual moving direction with a surrogate direction that would occur if the obstacle were absent; if the angle is positive, the direction is left, otherwise right. Gesture commands then force replanning through artificial obstacle insertion, waiting, or ignoring the human obstacle depending on the command. The paper’s contribution is therefore not only socially aware planning but legible and revisable planning (Girgin et al., 2024).

"Game Theory to Study Cooperation in Human-Robot Mixed Groups: Exploring the Potential of the Public Good Game" (Pusceddu et al., 29 Sep 2025) examines whether a robot can foster cooperation and trust in mixed groups. The study uses a modified Public Good Game (PGG) with payoff

$s_t$3

with $s_t$4 players, one of whom is the humanoid robot iCub. Each player has €1 per round, can contribute €0, €0.50, or €1, the pool is multiplied by 1.6, and the game lasts 10 rounds. In the pilot, iCub used an always cooperate strategy, contributing €1 each round, while also greeting participants, appearing to think, producing small random behaviors, and looking at participants and the screen as if checking scores (Pusceddu et al., 29 Sep 2025).

The pilot involved 19 participants aged 9 to 15 years, mean age 12.4 ± 1.5 years. Across 190 total trials, the contribution distribution was 44.4% at €0, 34.1% at €0.50, and 21.4% at €1. Participants nonetheless rated iCub as generous, with mean generosity 4.2 / 5 and standard deviation 0.9, and often assigned it social roles: 47.4% friend, 21.1% neighbor, 21.1% classmate, 10.4% stranger, and 0% teacher or relative. The preliminary conclusion is therefore negative in a precise sense: a robot’s generosity alone may not be enough to induce cooperation in mixed groups (Pusceddu et al., 29 Sep 2025).

The strongest synthesis of communicable learning appears in "A Review of Communicating Robot Learning during Human-Robot Interaction" (Habibian et al., 2023). The review argues that robot learning and robot communication should be coupled into a closed loop: the human teaches, the robot learns, the robot communicates what it learned, and the human updates teaching accordingly. It distinguishes implicit communication, where humans infer learning from robot behavior, from explicit communication, where the robot intentionally renders its learned model through waypoints, saliency maps, language, or other cues. The reviewed modalities include visual, haptic, and auditory channels, with multimodal systems often preferred when modality assignment is careful (Habibian et al., 2023).

The case study in that paper used 12 in-person participants teaching a 7-degree-of-freedom Franka Emika robot arm to assemble a simple chair. The learning algorithm was human-gated DAgger with policy $s_t$5 and loss

$s_t$6

An ensemble

$s_t$7

provided an uncertainty estimate, with threshold $s_t$8. The within-subjects conditions were Implicit, GUI, and AR+Haptic. With explicit feedback, users corrected the robot on the correct chair leg in 100% of trials; under implicit feedback, they misread the robot’s intent about 35% of the time. Robot error was significantly lower in GUI and AR+Haptic than in Implicit, and AR+Haptic outperformed GUI on the objective measure (Habibian et al., 2023).

These papers converge on a common result: fostering trust or cooperation does not follow automatically from robot presence or even prosocial robot behavior. Legibility, bidirectionality, and calibrated communication are central. Generosity without incentive alignment may fail to elicit cooperation, while explicit communication of uncertainty or route choice can improve teaching, comfort, and co-adaptation.

7. Governance, evaluation, limitations, and open questions

Governance is a first-class concern in the most explicit robot-fostering framework (Pablo-Marti et al., 28 Sep 2025). The proposal assumes privacy-by-design from day zero, data minimization, on-device redaction, contractor and supply-chain controls, auditable processes, civilian-only use, and transparent oversight. It aligns deployment with the EU AI Act, GDPR Article 35 / DPIA, ISO/IEC 42001, ISO/IEC 23894, IEEE 7001, ISO 13482, and NIST AI RMF 1.0. The lifecycle is organized as Plan, DPIA, Design, Deploy, Monitor, and Audit. Proposed practices include redaction audits using synthetic fixtures like faces and plates, participant dashboards for inspection, audit, and deletion of records, visitor notices, opt-out capture, transparent complaint logs, stratified sampling to avoid socioeconomic skew, and non-coercive compensation (Pablo-Marti et al., 28 Sep 2025).

The same paper proposes a minimal research program over 12–24 months with three main experiments: curriculum vs control, tool-free vs tool-enabled evaluation, and hold-out homes with shift tests. Proposed metrics include success@3, FTFC@R, replan-cost@3, incidents/100h, post-encroachment time at doorways, and standardized bystander/user ratings adapted from ADR studies. A toy simulation uses phase success probabilities $s_t$9, implying chained success $a_t$0, to illustrate compounding curriculum effects. Appendix power analyses specify 64 homes per arm for a two-arm independent-samples $a_t$1-test with $a_t$2, power $a_t$3, and Cohen’s $a_t$4; 158 homes total for a three-arm ANOVA with Cohen’s $a_t$5; and 167 homes per arm to detect a proportion increase from 0.50 \rightarrow 0.65 under the same $a_t$6 and power. Resource-constrained alternatives include paired or repeated-measures designs, group-sequential monitoring, and Pocock boundaries for early stopping (Pablo-Marti et al., 28 Sep 2025).

Across the broader literature, the empirical base remains exploratory. The older-adult robotic pet study had only six participants, all German and described as tech-savvy, with a five male, one female gender imbalance and a sample mostly composed of married participants (Du et al., 9 Dec 2025). The Public Good Game paper reports only a pilot study with children/adolescents and only the always cooperate robot strategy tested so far (Pusceddu et al., 29 Sep 2025). The social navigation framework presents preliminary experiments, and the full navigation system is not yet complete (Girgin et al., 2024). The parent-robot collaboration study involved 10 families, all participating parents were mothers, and the sample was skewed toward higher education, higher household income, and mostly White families (Ho et al., 2024). The memory-based multitask architecture is evaluated on five tabletop tasks and reports model-specific variability, coordinator bottlenecks, and longer-horizon failure modes (Ali et al., 2024).

The open questions are correspondingly broad. The governance-first framework asks whether curricular domestic exposure actually outperforms unstructured exposure, which trajectory-quality metrics best predict generalization, how much annotation is worth its cost, whether privacy-by-design survives large-scale household deployment, whether households will accept fostering burdens and risks, and whether the approach generalizes across robot classes (Pablo-Marti et al., 28 Sep 2025). The relational studies ask how robots can support human-human ties without becoming social dead ends or misleading surrogates (Du et al., 9 Dec 2025, Shen et al., 31 Jan 2025). The collaboration and trust studies ask how initiative, explanation, uncertainty, and strategy should be tuned so that human expectations remain calibrated rather than merely positive (Ho et al., 2024, Habibian et al., 2023, Pusceddu et al., 29 Sep 2025).

In aggregate, robot fostering names a research direction in which robots are not evaluated solely as autonomous performers of isolated tasks. They are instead situated within long-term developmental processes: households curate their exposure, memory architectures preserve their competence, communicative interfaces render their learning legible, and relational designs position them as mediators or collaborators within human social systems. The literature does not yet establish a unified doctrine, but it consistently argues that durable robot capability and social value emerge from structured exposure, bidirectional adaptation, and governance-aware integration into everyday life.