Accelerated Reinforcement Learning for Soft Robotics: Hours Instead of Years
Overview
This research demonstrates that using adiabatic spectral submanifolds (aSSMs) to reduce the dynamics of soft robots allows reinforcement learning policies to be trained in hours instead of years. On a pneumatically actuated soft arm, a trajectory-tracking policy was trained in 3 hours on a six-dimensional aSSM-reduced model and deployed without fine-tuning. This approach achieved 40% lower tracking error compared to policies trained on larger recurrent neural networks (RNN) or transformers, which required significantly more time and data to identify.
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Reported quantity · training_hours: 3 hours · Basis not reported · Differing source assertions
“SSM-reduced model, a simple polynomial ordinary differential equation learned purely from pre-collected data, can serve as an RL training environment. On a pneumatically actuated soft arm, we train a trajectory-tracking policy in 3 hours on a six-dimensional aSSM-reduced model and deploy it without fine-tuning, whereas reproducing this policy training extrapolates to 3 years of hardware interaction. Our policy tracks”
Exact source · revision 1Source owner not reported
Reported quantity · training_hours: 100 hours · Basis not reported · Differing source assertions
“dynamic targets with 40% lower error than otherwise identical policies trained on a 4.8-million-parameter recurrent neural network (RNN) or a 1.7-million-parameter transformer, which take 13 and 100 hours to identify, against 5 minutes for our mathematically justified 6,924-parameter model.”
Exact source · revision 1Source owner not reported
Reported quantity · training_minutes: 5 other · Basis not reported
“dynamic targets with 40% lower error than otherwise identical policies trained on a 4.8-million-parameter recurrent neural network (RNN) or a 1.7-million-parameter transformer, which take 13 and 100 hours to identify, against 5 minutes for our mathematically justified 6,924-parameter model.”
Exact source · revision 1Source owner not reported
Reported quantity · parameter_count: 6924 other · Basis not reported
“dynamic targets with 40% lower error than otherwise identical policies trained on a 4.8-million-parameter recurrent neural network (RNN) or a 1.7-million-parameter transformer, which take 13 and 100 hours to identify, against 5 minutes for our mathematically justified 6,924-parameter model.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalAccelerated Reinforcement Learning for Soft Robotics: Hours Instead of Years
This research demonstrates that using adiabatic spectral submanifolds (aSSMs) to reduce the dynamics of soft robots allows reinforcement learning policies to be trained in hours instead of years. On a pneumatically actuated soft arm, a trajectory-tracking policy was trained in 3 hours on a six-dimensional aSSM-reduced model and deployed without fine-tuning. This approach achieved 40% lower tracking error compared to policies trained on larger recurrent neural networks (RNN) or transformers, which required significantly more time and data to identify.
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