Accelerating Interactive Human-like Manipulation Learning with GPU-based
Simulation and High-quality Demonstrations
- URL: http://arxiv.org/abs/2212.02126v1
- Date: Mon, 5 Dec 2022 09:37:27 GMT
- Title: Accelerating Interactive Human-like Manipulation Learning with GPU-based
Simulation and High-quality Demonstrations
- Authors: Malte Mosbach, Kara Moraw, Sven Behnke
- Abstract summary: We present an immersive virtual reality teleoperation interface designed for interactive human-like manipulation on contact rich tasks.
We demonstrate the complementary strengths of massively parallel RL and imitation learning, yielding robust and natural behaviors.
- Score: 25.393382192511716
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Dexterous manipulation with anthropomorphic robot hands remains a challenging
problem in robotics because of the high-dimensional state and action spaces and
complex contacts. Nevertheless, skillful closed-loop manipulation is required
to enable humanoid robots to operate in unstructured real-world environments.
Reinforcement learning (RL) has traditionally imposed enormous interaction data
requirements for optimizing such complex control problems. We introduce a new
framework that leverages recent advances in GPU-based simulation along with the
strength of imitation learning in guiding policy search towards promising
behaviors to make RL training feasible in these domains. To this end, we
present an immersive virtual reality teleoperation interface designed for
interactive human-like manipulation on contact rich tasks and a suite of
manipulation environments inspired by tasks of daily living. Finally, we
demonstrate the complementary strengths of massively parallel RL and imitation
learning, yielding robust and natural behaviors. Videos of trained policies,
our source code, and the collected demonstration datasets are available at
https://maltemosbach.github.io/interactive_ human_like_manipulation/.
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