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Found 11 papers, 1 papers with code
Off-Policy Average Reward Actor-Critic with Deterministic Policy Search
In this work, we present both on-policy and off-policy deterministic policy gradient theorems for the average reward performance criterion.
Improving Sample Efficiency in Evolutionary RL Using Off-Policy Ranking
This leads to a lot of wasteful interactions since, once the ranking is done, only the data associated with the top-ranked policies is used for subsequent learning.
Learning Linear Policies for Robust Bipedal Locomotion on Terrains with Varying Slopes
In this paper, with a view toward deployment of light-weight control frameworks for bipedal walking robots, we realize end-foot trajectories that are shaped by a single linear feedback policy.
Imitation Learning for High Precision Peg-in-Hole Tasks
Industrial robot manipulators are not able to match the precision and speed with which humans are able to execute contact rich tasks even to this day.
Multi-Instance Aware Localization for End-to-End Imitation Learning
Existing architectures for imitation learning using image-to-action policy networks perform poorly when presented with an input image containing multiple instances of the object of interest, especially when the number of expert demonstrations available for training are limited.
Stochastic Action Prediction for Imitation Learning
Imitation learning is a data-driven approach to acquiring skills that relies on expert demonstrations to learn a policy that maps observations to actions.
Teaching Robots Novel Objects by Pointing at Them
Robots that must operate in novel environments and collaborate with humans must be capable of acquiring new knowledge from human experts during operation.
Robust Quadrupedal Locomotion on Sloped Terrains: A Linear Policy Approach
In particular, the parameters of the end-foot trajectories are shaped via a linear feedback policy that takes the torso orientation and the terrain slope as inputs.
Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations
First, multiple simpler policies are trained to generate trajectories for a discrete set of target velocities and turning radius.
Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots
In this work, we provide a simulation framework to perform systematic studies on the effects of spinal joint compliance and actuation on bounding performance of a 16-DOF quadruped spined robot Stoch 2.
Realizing Learned Quadruped Locomotion Behaviors through Kinematic Motion Primitives
Leveraging on this underlying structure, we then realize walking in Stoch by a straightforward reconstruction of joint trajectories from kMPs.
