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Modelling and learning the dynamics of intricate dynamic interactions prevalent in common tasks such as push- ing a heavy door or picking up an object in one sweeping motion is a challenging problem. One needs to consider both the dynamics of the individual objects and of the interactions among objects. In this work, we present a method that enables efficient learning of the dynamics of interacting systems by simultaneously learning a dynamic graph structure and a stable and locally linear forward dynamic model of the system. The dynamic graph structure encodes evolving contact modes along a trajectory by making probabilistic predictions over the edge activations. Introducing a temporal dependence in the learned graph structure enables incorporating contact measurement updates which allows for more accurate forward predictions. The learned stable and locally linear dynamics enable the use of optimal control algorithms such as iLQR for long-horizon planning and control for complex interactive tasks. Through experiments in simulation and in the real world, we evaluate the performance of our method by using the learned inter- action dynamics for control and demonstrate generalization to more objects and interactions not seen during training. We also introduce a control scheme that takes advantage of contact measurement updates and hence is robust to prediction inaccuracies during execution.
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Learning Reactive and Predictive Differentiable Controllers for Switching Linear Dynamical Models
Humans leverage the dynamics of the environment and their own bodies to accomplish challenging tasks such as grasping an object while walking past it or pushing off a wall to turn a corner. Such tasks often involve switching dynamics as the robot makes and breaks contact. Learning these dynamics is a challenging problem and prone to model inaccuracies, especially near contact regions. In this work, we present a framework for learning composite dynamical behaviors from expert demonstrations. We learn a switching linear dynamical model with contacts encoded in switching conditions as a close approximation of our system dynamics. We then use discrete-time LQR as the differentiable policy class for data-efficient learning of control to develop a control strategy that operates over multiple dynamical modes and takes into account discontinuities due to contact. In addition to predicting interactions with the environment, our policy effectively reacts to inaccurate predictions such as unanticipated contacts. Through simulation and real world experiments, we demonstrate generalization of learned behaviors to different scenarios and robustness to model inaccuracies during execution.
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- PAR ID:
- 10293206
- Date Published:
- Journal Name:
- IEEE International Conference on Robotics and Automation
- ISSN:
- 1049-3492
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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