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Found 9 papers, 7 papers with code
FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning
Motion trajectories offer reliable references for physics-based motion learning but suffer from sparsity, particularly in regions that lack sufficient data coverage.
Learning Emergent Gaits with Decentralized Phase Oscillators: on the role of Observations, Rewards, and Feedback
We present a minimal phase oscillator model for learning quadrupedal locomotion.
Benchmarking Potential Based Rewards for Learning Humanoid Locomotion
Although several studies have explored the use of potential based reward shaping to accelerate learning convergence, most have been limited to grid-worlds and low-dimensional systems, and RL in robotics has predominantly relied on standard forms of reward shaping.
Safe Value Functions
Although it is often not possible to compute the minimum required penalty, we reveal clear structure of how the penalty, rewards, discount factor, and dynamics interact.
On exploration requirements for learning safety constraints
In particular, we discuss the family of constraints that enforce safety in the context of a nominal control policy, and expose that these constraints do not need to be accurate everywhere.
A Learnable Safety Measure
While safety can only be guaranteed after learning the safety measure, we show that failures can already be greatly reduced by using the estimated measure during learning.
An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research
Finally, to demonstrate the capabilities of the quadruped, we present a novel controller which combines feedforward contact forces computed from a kino-dynamic optimizer with impedance control of the center of mass and base orientation.
Robotics
Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics
Most studies of simple walking and running models have focused on the basins of attraction of passive limit-cycles and the notion of self-stability.
Robotics
Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fall with Grace
Despite impressive results using reinforcement learning to solve complex problems from scratch, in robotics this has still been largely limited to model-based learning with very informative reward functions.
