Pick-and-Reorient Task in Robotics

Updated 20 December 2025

The PnR task defines a manipulation challenge where robots sequence picks, intermediate reorientations, and placements to achieve specified poses while satisfying collision, stability, and kinematic constraints.
It integrates planning, perception, and learning methods, employing regrasp graphs, transformer models, and diffusion-based policies to improve task feasibility and efficiency.
The approach optimizes for stability, collision-free execution, and minimal motion cost, enabling reliable object transfers in assembly, packing, and other constrained applications.

The Pick-and-Reorient (PnR) task encompasses a class of robotic manipulation problems focused on transferring an object from an initial pose to a target pose, addressing geometric, kinematic, or environmental constraints that preclude direct placement. PnR requires either manipulation through intermediate placements—including regrasps, support utilization, or in-hand reorientation—or specialized motion primitives such as pivoting, rolling, or press-and-place. The PnR task is central in robotics for applications where orientation is critical (e.g., assembly, packing, or placing in constrained regions) and involves a combination of perception, planning, and control across both simulated and real-world platforms (Xu et al., 2022, Yuan et al., 2023, Wan et al., 2018, Hou et al., 2019, Cao et al., 2015, Mitash et al., 2020, Xu et al., 2021, Watanabe et al., 27 Sep 2025, Mishra et al., 2023, Huang et al., 2023).

1. Formal Problem Definition and Task Scope

The PnR task seeks to find a sequence of actions—spanning picks, placements, potential regrasps (single or dual arm), and possibly in-hand or support-based reorientations—that move an object from a known initial pose to a goal pose, satisfying collision, stability, and task-specific geometric constraints. Typical inputs include object and scene geometry from multi-modal perception (e.g., RGB-D, point clouds, voxel occupancy), goal region pose and clearance, manipulator configuration, and environment support information. Output is a full action sequence (or motion plan) that guarantees the object attains the specified pose and orientation, potentially through intermediate placements or transitional actions. Critical constraints include:

Collision avoidance with both environment and unknown object geometry (especially in tight placement tasks) (Mitash et al., 2020).
Stable intermediate and final object placements, often requiring predictive modeling of gravitational stability and contact mechanics (Cao et al., 2015, Xu et al., 2022).
Feasible, force-closure and kinematically reachable robot grasps across all placements (Wan et al., 2018, Cao et al., 2015).
Optimization objectives such as minimizing total execution time, number of motions, or planning cost (Xu et al., 2021).
For unknown objects, maintenance of conservative volumetric uncertainty to ensure safety (Mitash et al., 2020).

2. Representative Algorithmic Pipelines

PnR methods are diverse and exploit a combination of planning, learning, and geometric reasoning. Core algorithmic components are:

a) Regrasp/Manipulation Graph Construction

Nodes represent stable object placements (on table, support, or pin) and associated feasible grasps; edges encode transitions between placements sharing at least one common grasp (Cao et al., 2015, Wan et al., 2018, Xu et al., 2022).
Multi-layered graphs handle both placement transitions and in-hand/in-arm grasp switching, including dual-arm handoffs (Wan et al., 2018, Mitash et al., 2020).
Graph search algorithms (e.g., BFS, A*, Dijkstra) are used to discover the shortest or least-cost pick–reorient–place sequence, optimizing for minimal transitions and feasibility (Cao et al., 2015, Xu et al., 2022, Wan et al., 2018).

b) Perception and Placement Prediction

Segmented point clouds and RGB-D fusion enable detailed scene and object affordance encoding (Xu et al., 2022, Yuan et al., 2023).
Deep neural models (e.g., PointNet++, transformers) generate placement candidates, with distinct stages for pose generation, refinement (e.g., via learned forward-dynamics), and stability classification (Xu et al., 2022, Yuan et al., 2023).
Placement generation leverages loss functions such as Chamfer distance or joint multi-task objectives for combining mask, stability, and geometric objectives (Xu et al., 2022, Yuan et al., 2023).

c) Learning-Driven Planning and Policy Inference

Transformers and equivariant architectures (e.g., M2T2, Equivariant Transporter Net) predict contact masks and 6-DoF action poses directly from point clouds, facilitating pick, reorient, and place for arbitrary scenes (Yuan et al., 2023, Huang et al., 2023).
Generative diffusion models (ReorientDiff) condition on both scene inputs and goal-specific language prompts, sampling intermediate configurations via classifier-guided denoising and learned feasibility scores (Mishra et al., 2023).
Hierarchical planners combine high-level reorientation pose selection (e.g., via DQN) with low-level grasp and path planning, leveraging neural cost estimators and anytime prioritized search (Xu et al., 2021).

d) Action Chunking and Multimodal Control

End-to-end imitation learning models fuse visual, proprioceptive, and force–torque signals into transformer policies for robust PnR, especially in contact-rich edge cases (e.g., press-based upright bottle placement) (Watanabe et al., 27 Sep 2025).
Action chunking accelerates rollouts by predicting sequences of atomic actions, enhancing real-time PnR success in physical deployments (Watanabe et al., 27 Sep 2025).

e) Physics-Based and Extrinsic Dexterity Approaches

Pivot-on-support and roll-on-support primitives leverage passive rotational or sliding mechanics, supported by robust quasi-static analysis, to expand the achievable pose set under workspace or kinematic constraints (Hou et al., 2019).
Extrinsic supports such as vertical pins or table edges increase the set of stable interstitial placements, boosting manipulation graph connectivity and success rates (Cao et al., 2015, Xu et al., 2022).

3. Core Theoretical and Geometric Principles

PnR methods build upon several foundational theories:

Stable Placement Computation: Analytical determination of object equilibrium on supports, including convex hull analysis, barycentric stability, and friction cone constraints at the support or pin contact (Cao et al., 2015, Xu et al., 2022).
Manipulation Graph Topology: High graph connectivity correlates with increased PnR feasibility; support structures (pins, table edges) or object symmetry can expand the reachable configuration space (Cao et al., 2015, Huang et al., 2023).
Equivariance and Symmetry: Planar and rotational symmetries (e.g., $SE(2)$ ) are leveraged via equivariant neural architectures, which yield improved sample efficiency and better generalization (Huang et al., 2023).
Robustness to Uncertainty: Conservative planners (e.g., via S∪U voxel sets for unknown objects) guarantee collision-free execution in the absence of full object models (Mitash et al., 2020). Hybrid control schemes handle pivoting/rolling transitions in the presence of friction and mass uncertainty (Hou et al., 2019).

4. Benchmarks, Experimental Approaches, and Quantitative Results

Research groups validate PnR methods via both synthetic and real-world experiments, using metrics such as placement accuracy (translation/orientation deviation), diversity of stable placements, reorientation success rates, and execution cost.

Method	Placement Accuracy	Reorientation Success	Notes
Pipeline w/ support (PtNet++) (Xu et al., 2022)	36.9%	(20.2 pp > baseline)	Outperforms L2P; more diverse placements
M2T2 Transformer (Yuan et al., 2023)	Place+Reorient 62.5%	+37.5 pp > Contact-GraspNet+CabiNet	State-of-the-art for unseen objects
Equivariant Transporter (Huang et al., 2023)	>99% (block insertion)	Rapid SGD convergence	Sample-efficient imitation learning
ReorientDiff (Diffusion) (Mishra et al., 2023)	95.2%	—	Highest on YCB; fast inference
FTACT (ACT+FT) (Watanabe et al., 27 Sep 2025)	100% (trained)	80% (untrained)	Force–torque improves contact-rich PnR
Pin-Enhanced (Cao et al., 2015)	85% (pot-lid)	Increases workspace	Pin length tuning critical; >planar-only

Experimental scenarios span household/industrial objects, YCB sets, and challenging bottle or assembly tasks, using single/double arm robots, with or without prior object models. Approaches with active sensing drastically reduce necessary viewpoints and planning time while maintaining collision guarantees for unknown objects (Mitash et al., 2020).

5. Extensions: Multi-Arm, In-Hand, Language, and Sim-to-Real

Extensions of the PnR paradigm accommodate:

Single/Dual Arm and Handover: Super-graph constructions integrate single-arm, dual-arm, and combined regrasp/handover transitions, selected automatically via search (Wan et al., 2018, Mitash et al., 2020).
Language-Instructed Reorientation: Joint scene–task encodings (e.g., via CLIP) enable diffusion models to adapt to flexible goal prompts, generalizing the PnR policy to novel or under-specified tasks (Mishra et al., 2023).
In-Hand Reorientation and Pivoting: Motion primitives beyond pick–place, such as pivot/roll, enable efficient coverage under kinematic, spatial, or contact limits (Hou et al., 2019) [(2512.04095)*].
Force and Tactile Sensing: Multimodal policies incorporate interaction forces for robust execution in contact-rich sub-phases (e.g., upright bottle placement when vision alone is insufficient) (Watanabe et al., 27 Sep 2025).
Sim-to-Real Bridging: Most modern pipelines (e.g., M2T2, ReorientDiff) demonstrate high (typically >60%) zero-shot transfer to hardware, with error/failure modes attributed largely to unseen mechanical properties or dynamic effects (Yuan et al., 2023, Mishra et al., 2023).

6. Open Problems, Limitations, and Theoretical Guarantees

Key open challenges and constraints in PnR include:

Model Uncertainty: Conservative volumetric or classifier-based feasibility predictions are critical when full object geometry is unknown; coverage is limited by the fidelity of simulation or real-world demonstrations (Mitash et al., 2020, Mishra et al., 2023).
Multi-Step Reorientation: Most pipelines consider only one or two intermediate placements; complex objects or constraints may require richer intermediate sequence planning, posing scalability issues for graph search (Xu et al., 2021, Hou et al., 2019).
Sampling and Graph Completeness: The density of sampled placements/grasps influences completeness; support pin length and placement geometry are crucial parameters (Cao et al., 2015, Xu et al., 2022).
Generalization: Although transformer and diffusion-based policies show strong sim-to-real transfer, unmodeled dynamic and frictional conditions, or geometric outliers, still present failure modes (Yuan et al., 2023, Watanabe et al., 27 Sep 2025).
Planning Speed vs. Optimality: Anytime search and neural cost estimators accelerate planning but may not always guarantee minimum path cost without further refinement (Xu et al., 2021).

The PnR task remains a benchmark for studying the intersection of geometric reasoning, data-driven policy learning, and hybrid robot control in object-centric manipulation across an evolving range of practical and research scenarios. Continued work addresses robustness, multi-modal generalization, and integration with higher-level reasoning (e.g., language and scene semantics).

References

Learning to Reorient Objects with Stable Placements Afforded by Extrinsic Supports (Xu et al., 2022)
Task-driven Perception and Manipulation for Constrained Placement of Unknown Objects (Mitash et al., 2020)
M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place (Yuan et al., 2023)
Analyzing the Utility of a Support Pin in Sequential Robotic Manipulation (Cao et al., 2015)
Efficient Object Manipulation to an Arbitrary Goal Pose: Learning-based Anytime Prioritized Planning (Xu et al., 2021)
Leveraging Symmetries in Pick and Place (Huang et al., 2023)
FTACT: Force Torque aware Action Chunking Transformer for Pick-and-Reorient Bottle Task (Watanabe et al., 27 Sep 2025)
Reorienting Objects in 3D Space Using Pivoting (Hou et al., 2019)
Preparatory Manipulation Planning using Automatically Determined Single and Dual Arms (Wan et al., 2018)
ReorientDiff: Diffusion Model based Reorientation for Object Manipulation (Mishra et al., 2023)