From a Single Demonstration to a General Policy for Contact-Rich Manipulation

• URL: http://arxiv.org/abs/2605.17601v1
• Date: Sun, 17 May 2026 18:58:03 GMT
• Title: From a Single Demonstration to a General Policy for Contact-Rich Manipulation
• Abstract summary: We present a framework that achieves one-shot generalization in contact-rich manipulation tasks.<n>By representing a demonstration as a sequence of behaviors that exploit environmental constraints, the robot separates task-general structure.<n>We validate our approach on seven real-world contact-rich manipulation tasks and achieve over 90% success.
• Score: 16.799592363547337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a Learning from Demonstration (LfD) framework that achieves one-shot generalization in multi-stage, contact-rich manipulation tasks. Central to our approach is the utilization of environmental constraints as the inductive bias. By representing a demonstration as a sequence of behaviors that exploit environmental constraints, the robot separates task-general structure -- the constraint types and their transitions -- from instance-specific details such as exact demonstration trajectories, poses, and local geometries. Our four-stage pipeline builds a complete policy on this representation: the robot first abstracts a single demonstration into environmental-constraint primitives, then disambiguates them through self-guided exploration, next assimilates targeted human corrections that handle out-of-distribution variations, and finally recovers the abstracted-away details online through compliant interaction. Because the resulting policy follows constraints rather than mimics trajectories, it generalizes across object poses, local geometries, and unmodeled contact dynamics. We validate our approach on seven real-world multi-stage contact-rich manipulation tasks and achieve over 90% success. These extensive experimental results establish environmental constraints as fundamental building blocks for efficient generalization in learning from demonstration.

Related papers

• Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking [78.30338456206984]
  We introduce a novel framework for training general-purpose 3D foothold-tracking policies.<n>By dynamically providing footstep support through a goal sampler, this method enables the learned policy to be agnostic to specific terrains.<n>Our new target representation effectively mitigates challenges arising in the real world, such as noisy and inaccurate pose estimation and foot contact estimation.
  arXiv  Detail & Related papers  (2026-06-06T16:44:56Z)
• Multi-Modal Manipulation via Multi-Modal Policy Consensus [62.49978559936122]
  We propose a new approach to integrate diverse sensory modalities for robotic manipulation.<n>Our method factorizes the policy into a set of diffusion models, each specialized for a single representation.<n>We evaluate our approach on simulated manipulation tasks in RLBench, as well as real-world tasks such as occluded object picking, in-hand spoon reorientation, and puzzle insertion.
  arXiv  Detail & Related papers  (2025-09-27T19:43:04Z)
• ImaginationPolicy: Towards Generalizable, Precise and Reliable End-to-End Policy for Robotic Manipulation [46.06124092071133]
  We propose a novel Chain of Moving Oriented Keypoints (CoMOK) formulation for robotic manipulation.<n>Our formulation is used as the action representation of a neural policy, which can be trained in an end-to-end fashion.
  arXiv  Detail & Related papers  (2025-09-25T07:29:07Z)
• Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration [58.4036440289082]
  Hand-object motion-capture (MoCap) offer large-scale, contact-rich demonstrations and hold promise for dexterous robotic scopes.<n>We introduce Dexplore, a unified single-loop optimization that performs repositories and tracking to learn robot control policies directly from MoCap at scale.
  arXiv  Detail & Related papers  (2025-09-11T17:59:07Z)
• Unlocking Smarter Device Control: Foresighted Planning with a World Model-Driven Code Execution Approach [82.27842884709378]
  We propose a framework that prioritizes natural language understanding and structured reasoning to enhance the agent's global understanding of the environment.<n>Our method outperforms previous approaches, particularly achieving a 44.4% relative improvement in task success rate.
  arXiv  Detail & Related papers  (2025-05-22T09:08:47Z)
• PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations [24.77819842428131]
  Reinforcement learning can autonomously explore to obtain robust behaviors.<n>Training RL agents through direct interaction with the real world is often impractical and unsafe.<n>We propose an integrated real-to-sim-to-real pipeline that constructs simulation environments based on expert demonstrations.
  arXiv  Detail & Related papers  (2025-04-29T08:01:27Z)
• Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation [16.244250979166214]
  Generalizable Planning-Guided Diffusion Policy Learning (GLIDE) is an approach that learns to solve contact-rich bimanual manipulation tasks.<n>We propose a set of essential design options in feature extraction, task representation, action prediction, and data augmentation.<n>Our approach can enable a bimanual robotic system to effectively manipulate objects of diverse geometries, dimensions, and physical properties.
  arXiv  Detail & Related papers  (2024-12-03T18:51:39Z)
• Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames [10.738838923944876]
  Existing methods require a substantial number of demonstrations to cover possible task variations.<n>We introduce oriented affordance frames, a structured representation for state and action spaces.<n>We show how this abstraction allows for compositional generalisation of independently trained sub-policies.<n>We validate our method across three real-world tasks, each requiring multi-step, multi-object interactions.
  arXiv  Detail & Related papers  (2024-10-15T23:57:35Z)
• Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors [72.62423312645953]
  Humans intuitively solve tasks in versatile ways, varying their behavior in terms of trajectory-based planning and for individual steps. Current Imitation Learning algorithms often only consider unimodal expert demonstrations and act in a state-action-based setting. Instead, we combine a mixture of movement primitives with a distribution matching objective to learn versatile behaviors that match the expert's behavior and versatility.
  arXiv  Detail & Related papers  (2022-10-17T16:42:59Z)
• Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations [52.696205074092006]
  Generalization Through Imitation (GTI) is a two-stage offline imitation learning algorithm. GTI exploits a structure where demonstrated trajectories for different tasks intersect at common regions of the state space. In the first stage of GTI, we train a policy that leverages intersections to have the capacity to compose behaviors from different demonstration trajectories together. In the second stage of GTI, we train a goal-directed agent to generalize to novel start and goal configurations.
  arXiv  Detail & Related papers  (2020-03-13T02:25:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.