Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models

06/22/2022
by   Tao Pang, et al.
0

The empirical success of Reinforcement Learning (RL) in the setting of contact-rich manipulation leaves much to be understood from a model-based perspective, where the key difficulties are often attributed to (i) the explosion of contact modes, (ii) stiff, non-smooth contact dynamics and the resulting exploding / discontinuous gradients, and (iii) the non-convexity of the planning problem. The stochastic nature of RL addresses (i) and (ii) by effectively sampling and averaging the contact modes. On the other hand, model-based methods have tackled the same challenges by smoothing contact dynamics analytically. Our first contribution is to establish the theoretical equivalence of the two methods for simple systems, and provide qualitative and empirical equivalence on a number of complex examples. In order to further alleviate (ii), our second contribution is a convex, differentiable and quasi-dynamic formulation of contact dynamics, which is amenable to both smoothing schemes, and has proven through experiments to be highly effective for contact-rich planning. Our final contribution resolves (iii), where we show that classical sampling-based motion planning algorithms can be effective in global planning when contact modes are abstracted via smoothing. Applying our method on a collection of challenging contact-rich manipulation tasks, we demonstrate that efficient model-based motion planning can achieve results comparable to RL with dramatically less computation. Video: https://youtu.be/12Ew4xC-VwA

READ FULL TEXT

page 1

page 11

page 15

page 16

page 17

research
09/11/2021

Bundled Gradients through Contact via Randomized Smoothing

The empirical success of derivative-free methods in reinforcement learni...
research
10/25/2022

Quasistatic contact-rich manipulation via linear complementarity quadratic programming

Contact-rich manipulation is challenging due to dynamically-changing phy...
research
11/23/2020

COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing

General contact-rich manipulation problems are long-standing challenges ...
research
09/11/2019

Probabilistic Model Learning and Long-term Prediction for Contact-rich Manipulation Tasks

Learning dynamics models is an essential component of model-based reinfo...
research
11/03/2020

Efficient Sampling of Transition Constraints for Motion Planning under Sliding Contacts

Contact-based motion planning for manipulation, object exploration or ba...
research
11/08/2018

Decidability in Robot Manipulation Planning

Consider the problem of planning collision-free motion of n objects in t...
research
02/09/2019

A Quasi-static Model and Simulation Approach for Pushing, Grasping, and Jamming

Quasi-static models of robotic motion with frictional contact provide a ...

Please sign up or login with your details

Forgot password? Click here to reset