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Found 10 papers, 3 papers with code
RMPflow: A Computational Graph for Automatic Motion Policy Generation
We develop a novel policy synthesis algorithm, RMPflow, based on geometrically consistent transformations of Riemannian Motion Policies (RMPs).
Robotics Systems and Control
Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
The complex motions are encoded as rollouts of a stable dynamical system, which, under a change of coordinates defined by a diffeomorphism, is equivalent to a simple, hand-specified dynamical system.
Predictor-Corrector Policy Optimization
We present a predictor-corrector framework, called PicCoLO, that can transform a first-order model-free reinforcement or imitation learning algorithm into a new hybrid method that leverages predictive models to accelerate policy learning.
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
In doing so, we are able to change the distribution of simulations to improve the policy transfer by matching the policy behavior in simulation and the real world.
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping
RMPfusion supplements RMPflow with weight functions that can hierarchically reshape the Lyapunov functions of the subtask RMPs according to the current configuration of the robot and environment.
DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System
Teleoperation offers the possibility of imparting robotic systems with sophisticated reasoning skills, intuition, and creativity to perform tasks.
Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state.
Towards Coordinated Robot Motions: End-to-End Learning of Motion Policies on Transform Trees
The policy structure provides the user an interface to 1) specifying the spaces that are directly relevant to the completion of the tasks, and 2) designing policies for certain tasks that do not need to be learned.
RMP2: A Structured Composable Policy Class for Robot Learning
Using RMPflow as a structured policy class in learning has several benefits, such as sufficient expressiveness, the flexibility to inject different levels of prior knowledge as well as the ability to transfer policies between robots.
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories
Many IL methods, such as Dataset Aggregation (DAgger), combat challenges like distributional shift by interacting with oracular experts.
