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Found 8 papers, 4 papers with code
MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy
We introduce MORPH, a method for co-optimization of hardware design parameters and control policies in simulation using reinforcement learning.
Decision Making for Human-in-the-loop Robotic Agents via Uncertainty-Aware Reinforcement Learning
In a Human-in-the-Loop paradigm, a robotic agent is able to act mostly autonomously in solving a task, but can request help from an external expert when needed.
UMPNet: Universal Manipulation Policy Network for Articulated Objects
We introduce the Universal Manipulation Policy Network (UMPNet) -- a single image-based policy network that infers closed-loop action sequences for manipulating arbitrary articulated objects.
Learning 3D Dynamic Scene Representations for Robot Manipulation
3D scene representation for robot manipulation should capture three key object properties: permanency -- objects that become occluded over time continue to exist; amodal completeness -- objects have 3D occupancy, even if only partial observations are available; spatiotemporal continuity -- the movement of each object is continuous over space and time.
SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks
This paper aims to address this scalability challenge with a robust, sample-efficient, and general meta-IRL algorithm, SQUIRL, that performs a new but related long-horizon task robustly given only a single video demonstration.
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Therefore, if the aim of these methods is to enable faster acquisition of entirely new behaviors, we must evaluate them on task distributions that are sufficiently broad to enable generalization to new behaviors.
Ranked #1 on
Meta-Learning
on ML10
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment, run in parallel with the real robot.
Scaling simulation-to-real transfer by learning composable robot skills
In particular, we first use simulation to jointly learn a policy for a set of low-level skills, and a "skill embedding" parameterization which can be used to compose them.
