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Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control
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Compliance plays a crucial role in manipulation, as it balances between the concurrent control of position and force under uncertainties. Yet compliance is often overlooked by today's visuomotor policies that solely focus on position control. This paper introduces Adaptive Compliance Policy (ACP), a novel framework that learns to dynamically adjust system compliance both spatially and temporally for given manipulation tasks from human demonstrations, improving upon previous approaches that rely on pre-selected compliance parameters or assume uniform constant stiffness. However, computing full compliance parameters from human demonstrations is an ill-defined problem. Instead, we estimate an approximate compliance profile with two useful properties: avoiding large contact forces and encouraging accurate tracking. Our approach enables robots to handle complex contact-rich manipulation tasks and achieves over 50\% performance improvement compared to state-of-the-art visuomotor policy methods. For result videos, see https://adaptive-compliance.github.io/
Forward citations
Cited by 7 Pith papers
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FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots
FACET trains legged robots to track a virtual mass-spring-damper reference, so the user can tune stiffness and virtual mass to control how the robot yields to or applies forces.
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$N_0$-TWAM: Scaling Tactile-Native World-Action Model for Contact-Rich Manipulation
A scaled tactile-native world-action model jointly predicts vision, touch, and action and outperforms vision-only baselines on contact-rich sim and real robot tasks.
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TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models
Feeding torque history as a single decoder token and adding torque prediction as an auxiliary objective improves pretrained VLA success rates on contact-rich manipulation, with large gains on button pushing and charge...
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SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space
Continuous SE(3) equivariance is embedded in the policy by representing states, actions, and denoising steps in spherical Fourier space, improving generalization to novel 3D arrangements.
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FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation
FoAR uses a future-contact predictor to gate force/torque features into a vision-based imitation policy and adds a reactive nudge, beating vision-only and naive fusion baselines on three real contact-rich tasks.
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ALPHA-$\alpha$ and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System
Using force information from bilateral control improves imitation learning on unfamiliar objects, and a new low-cost ALPHA-alpha platform supports bimanual tasks.
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An Optimization-Augmented Control Framework for Single and Coordinated Multi-Arm Robotic Manipulation
A multi-modal controller that switches between optimization-based planning and force control completes simulated single-arm, bimanual, and four-arm manipulation tasks.
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