Training a high-dimensional simulated agent with an under-specified reward function often leads the agent to learn physically infeasible strategies that are ineffective when deployed in the real world. To mitigate these unnatural behaviors, reinforcement learning practitioners often utilize complex reward functions that encourage physically plausible behaviors. However, a tedious labor-intensive tuning process is often required to create hand-designed rewards which might not easily generalize across platforms and tasks. We propose substituting complex reward functions with "style rewards" learned from a dataset of motion capture demonstrations. A learned style reward can be combined with an arbitrary task reward to train policies that perform tasks using naturalistic strategies. These natural strategies can also facilitate transfer to the real world. We build upon Adversarial Motion Priorsan approach from the computer graphics domain that encodes a style reward from a dataset of reference motions -to demonstrate that an adversarial approach to training policies can produce behaviors that transfer to a real quadrupedal robot without requiring complex reward functions. We also demonstrate that an effective style reward can be learned from a few seconds of motion capture data gathered from a German Shepherd and leads to energy-efficient locomotion strategies with natural gait transitions.
Cables are ubiquitous in many settings, but are prone to self-occlusions and knots, making them difficult to perceive and manipulate. The challenge often increases with cable length: long cables require more complex slack management and strategies to facilitate observability and reachability. In this paper, we focus on autonomously untangling cables up to 3 meters in length using a bilateral robot. We develop new motion primitives to efficiently untangle long cables and novel gripper jaws specialized for this task. We present Sliding and Grasping for Tangle Manipulation (SGTM), an algorithm that composes these primitives with RGBD vision to iteratively untangle. SGTM untangles cables with success rates of 67% on isolated overhand and figure eight knots and 50% on more complex configurations. Supplementary material, visualizations, and videos can be found at https://sites.google.com/ view/rss-2022-untangling/home.
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