Contact-Rich Manipulation

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Actuator

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Robotic Arms

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Contact-Rich Manipulation

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Kinematics

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Ground Reaction Forces

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Inverse Kinematics

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Contact Forces

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Pose-Guided Kinematic Reasoning

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From a Single Demonstration to a General Policy for Contact-Rich Manipulation
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Apr 26, 2026Teng Xue, Alberto Rigo, Bingjian Huang +4Robotic Manipulation Policies Imitation Learning
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May 17, 2026 · cs.ROJ/K move · Enter open · S save

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

Xing Li, Oliver Brock

Robotics and Biology Laboratory, Technische Universität Berlin · Technische Universität Berlin · Science of Intelligence, Research Cluster of Excellence, Berlin · Robotics Institute Germany

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.

Robotic Manipulation Policies Real-World Manipulation Tasks Contact-Rich Manipulation Robot Systems Interaction Trajectories Few-Shot Learning Autonomous Exploration Strategies

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