Coordinated Robotic Manipulation

• Coordinated robotic manipulation is the planning, control, and execution of tasks by multiple robots working together to handle challenges infeasible for a single arm.
• It employs advanced methodologies such as multi-arm TAMP, constraint projection, and hybrid force control to address high-dimensional configuration spaces and collision avoidance.
• Recent approaches integrate deep learning, reinforcement learning, and vision-language models to enhance real-time adaptation, scalability, and robustness in various industrial applications.

Coordinated robotic manipulation encompasses the planning, control, and execution of manipulation tasks involving two or more robots acting in concert to achieve goals that are infeasible, inefficient, or unsafe for a single manipulator. It is central to automation in manufacturing, logistics, service robotics, and field operations, particularly in scenarios involving large, heavy, or articulated objects, workspace constraints, and dynamic, task-induced constraints. The field spans algorithmic frameworks for multi-arm motion and task planning, control theory for synchronizing force and movement, machine learning for robust policy synthesis, and system architectures that ensure real-time, practical deployment.

1. Problem Formulation and Fundamental Challenges

Coordinated robotic manipulation is fundamentally distinguished by the exponential increase in the configuration space dimensionality as the number of robots rises. For $n$ arms with individual configuration spaces $C_{m_1}, \dots, C_{m_n}$ of respective dimensions $d_1, \dots, d_n$, the composite configuration space is the Cartesian product $$C = C_{m_1} \times C_{m_2} \times \dots \times C_{m_n} \subset \mathbb{R}^{d_1 + d_2 + \dots + d_n}$$ (Shome et al., 2019). This leads to combinatorial complexity both in geometric collision-avoidance (self- and inter-arm), and in the explosion of possible operations (pick, place, handoff, simultaneous grasp, etc.).

Additional challenges include:

• Mode transitions and handoffs. Tasks such as pick-and-place with multiple arms require reasoning about discrete “mode” transitions (e.g., from single-arm grasp to handoff between arms). Ensuring feasibility and optimality in these transitions is non-trivial (Shome et al., 2019).
• Constraint coupling. Physical constraints augment coordination complexity, from closed-chain kinematics (rigidly grasped shared objects) to environmental constraints (nonholonomic bases, workspace obstacles) (Agrawal et al., 29 Oct 2024, Jiao et al., 2021).
• Task-level assignment with precedence and accessibility. Deciding which robot manipulates which object under accessibility, occlusion, and order constraints is often solved with combinatorial and graph-based methods (Zhang et al., 2023, Ahn et al., 2021).
• Sensing and perception. Robustly perceiving and modeling the state of all robots, objects, and environments—often distributed in space and time—is foundational for safe coordination (Song et al., 5 Aug 2025, Yang et al., 2023).

2. Algorithmic Frameworks

Task and Motion Planning (TAMP) for Multi-Arm Systems

State-of-the-art approaches to TAMP in multi-arm manipulation construct and search composite graphs that encode both geometric feasibility (motion) and symbolic task progress (task modes):

• dRRT* with multi-modal extension (Shome et al., 2019): Leverages individual probabilistic roadmaps for each arm and explores their tensor product space, integrated with a “mode graph” where vertices represent task states (pick, handoff, place) and edges encode allowable transitions. Directly exploring the composite space yields computational and solution-quality improvements.
• Collaborative Manipulation Task Graphs (CMTG) (Zhang et al., 2023): Build graphs capturing handover possibilities, reachability, and occlusion constraints. Mixed-integer programs (MILP) are constructed to minimize total object movements, subject to blocking and capability constraints; search over action sequences is guided by Monte-Carlo Tree Search (MCTS) with upper confidence bound (UCB) heuristics.
• Real-time supervisory learning control (Witte et al., 2023): Decouples discrete (task) and continuous (motion) planning, with a high-level supervisor trained via deep reinforcement learning to query subtasks to individual arms and enable rapid re-planning and adaptation.

Constraint Projection and Unified Manifold Methods

Complex cooperative mobile manipulation tasks demand enforcement of a large, heterogeneous set of simultaneous constraints:

• Constrained Nonlinear Kaczmarz (cNKZ) projection (Agrawal et al., 29 Oct 2024): Models the full constraint set as a family of nonlinear manifolds and iteratively projects candidate configurations onto the intersection, each constraint having its own residual threshold. This method successfully allows real-time planning for teams of mobile manipulators under up to 80 concurrent constraints.

Optimization-Augmented and Hybrid Control

Robust, compliant multi-arm manipulation requires switching between various control modes and integrating force control:

• Hybrid optimization-augmented force control (Özcan et al., 19 Jun 2025): The framework dynamically assigns subtasks to pure optimization (for free-space planning), pure force control (for high-frequency force tasks), or a hybrid mode (for simultaneous trajectory/force requirements in closed-chain manipulation). Task decomposition and seamless control mode switching are explicitly engineered to handle both collision avoidance and compliant force regulation in tightly coupled multi-arm systems.

3. Learning-Based and Latent Coordination Approaches

Recent advances integrate deep learning and probabilistic modeling for synchronized multi-robot policy synthesis.

• Central Latent Action Spaces (CLAS) (Aljalbout et al., 2022): Introduces a variational autoencoder-based framework in which a shared low-dimensional latent space coordinates all agents, decoupling high-dimensional joint action generation from object-level goals. The method outperforms decentralized and centralized baselines in high-DoF settings, especially as the number of arms increases.
• Hierarchical Diffusion Policy (HDP) (Ma et al., 6 Mar 2024): Factorizes policy generation into high-level strategic task planning (next-best pose, with Perceiver-Actor) and low-level goal-conditioned diffusion networks, with a kinematics-aware “RK-Diffuser” that learns to generate both end-effector and joint trajectories. Alignment between the two is enforced with differentiable kinematics, producing both accurate and physically feasible trajectories in articulated manipulation.
• Vision-language-guided simultaneous collaborative manipulation (Song et al., 5 Aug 2025): The CollaBot framework develops a scalable architecture for simultaneous collaborative grasp and manipulation, using VLMs/LLMs for object selection and motion constraint inference, LoGNet for local grasp generation, in-context evaluation for global grasp assignment, and a two-stage planning module for collision-free, closed-chain object transport. The system automatically infers the minimal required robot set and enforces closed-chain constraints along execution.

A summary table highlights the dominant algorithmic paradigms for coordinated robotic manipulation:

Approach	Key Principle	Reference
Multi-modal dRRT*, TAMP	Composite mode/configuration graph, sampling-based	(Shome et al., 2019)
Constraint Manifold Projection	Unified manifold constraints, cNKZ projection	(Agrawal et al., 29 Oct 2024)
Hybrid Optimization-Augmented Control	Sequential force/opt planning, hybrid control law	(Özcan et al., 19 Jun 2025)
Latent Representation Learning	VAE latent space or diffusion, coordinated decoding	(Aljalbout et al., 2022, Ma et al., 6 Mar 2024)
Foundation Model-based Perception/Grasp	VLM/LLM for segmentation, semantic & geometric cues	(Song et al., 5 Aug 2025)

4. Handoffs, Closed-Chain Constraints and Physical Coordination

Handoffs between arms are a critical primitive in many settings:

• The formalism in (Shome et al., 2019) models handoff states as pairs of end-effector configurations $$(q_{\mathrm{pick}}^1, g^1), (q_{\mathrm{pick}}^2, g^2)$$ and constrains the search to satisfy the necessary geometric conditions using a Transition Sampler, which computes valid inverse kinematic solutions.
• Closed-chain manipulation, as found in coordinated multi-arm transport or object reorientation, imposes rigid geometric relationships between the robots’ end-effectors, commonly enforced as equality constraints in motion planning (Song et al., 5 Aug 2025, Özcan et al., 19 Jun 2025, Agrawal et al., 29 Oct 2024).

Success in closed-chain multi-arm manipulation requires:

• Explicit constraint satisfaction at all steps.
• Collision-free trajectory planning accounting for redundant degrees of freedom.
• Robust force/torque regulation to prevent breakage of the chain due to external disturbances or internal tracking error.

5. Applications and Empirical Performance

Coordinated robotic manipulation frameworks are validated in a range of real-world and simulated tasks:

• Warehouse and manufacturing: Multi-arm pick-and-place, part handoff, assembly line reconfiguration (Shome et al., 2019).
• Cluttered object retrieval: Dual-arm turn-taking algorithms speed up execution by $22.9$–$27.3$\% over non-coordinated baselines (Ahn et al., 2021).
• Mobile and modular manipulation: Gap bridging with distributed cable-driven robots (CDPRs) for field robotics and construction, demonstrating scalable payload manipulation with low operator force input (Murphy et al., 19 Mar 2024).
• Dual-arm/ bimanual/ multi-arm cooperative manipulation: Systems achieve high success rates in transporting, assembling, and sorting objects with tight physical synchronization, maintaining constraints in orientation and force under dynamic and uncertain conditions (Özcan et al., 19 Jun 2025, Zhang et al., 2023, Bai et al., 2021).
• Service robotics and human-robot collaboration: Incorporating human input via teleoperation or policy retargeting for safety and dexterity in delicate manipulation or variable environments (Bai et al., 2021, Wen et al., 2022).

Strong empirical results include:

• Scalability to 5+ arms and over $10^5$-sized composite roadmaps (Shome et al., 2019).
• Hardware demonstrations with real-time replanning, turn-taking, and constraint satisfaction (Song et al., 5 Aug 2025, Agrawal et al., 29 Oct 2024).
• Success rates exceeding 90% in hierarchical learning-based supervisors for underactuated dual-manipulator pick-and-place (Witte et al., 2023).

6. Emerging Trends and Open Problems

Recent advancements indicate several directions:

• Hierarchical co-design: Simultaneous optimization of manipulator morphology and manipulation policy, incorporating caging-based robustness metrics such as minimum escape energy to handle contact model uncertainties (Dong et al., 17 Sep 2024).
• Distributed learning and privacy: Federated learning protocols (FLAME) enable policy aggregation across simulated robots facing diverse environments without centralizing data, directly addressing scalability and privacy (Betran et al., 3 Mar 2025).
• Integrated vision-language and machine learning models: The fusion of foundation models (SEEM, VLMs, LLMs) with classical and deep learning-based manipulation pipelines now enables task and object selection, grasp planning, and constraint extraction from natural language, supporting general-purpose, real-world deployment (Song et al., 5 Aug 2025, Yang et al., 2023).

Persistent challenges include:

• Automated decomposition of complex manipulation tasks into optimally assigned subtasks.
• Provably complete, rapid solvers for joint planning and control under high-dimensional, highly constrained settings.
• Robust sim-to-real transfer for learned multi-arm coordinated policies in the presence of dynamic uncertainties.
• Real-time adaptation and collision-resilient planning for teams involving mobile bases and manipulators in unstructured settings.

7. Conclusion

Coordinated robotic manipulation represents a convergence of high-dimensional task and motion planning, constraint satisfaction, real-time control, and perception-driven learning. The synthesis of graph-based search (mode configuration graphs, collaborative task graphs), advanced constraint projection (cNKZ), optimization-augmented and hybrid force control, as well as foundational advances in machine learning for latent space coordination, now enables reliable multi-arm systems to execute challenging collaborative tasks in constrained, dynamic environments. While scalability, robustness, and shared autonomy remain active research fronts, the collective progress outlined in recent literature sets a solid foundation for universal, coordinated manipulation across diverse industrial and service domains.