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Found 9 papers, 3 papers with code
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
With RoboCat, we demonstrate the ability to generalise to new tasks and robots, both zero-shot as well as through adaptation using only 100-1000 examples for the target task.
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration
The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents.
Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies
We demonstrate in manipulation domains that the method can effectively cluster offline data into distinct, executable behaviours, while retaining the flexibility of a continuous latent variable model.
Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration
Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks.
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning
Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives or constraints in the policy optimization step.
GRASPA 1.0: GRASPA is a Robot Arm graSping Performance benchmArk
In this paper, we present version 1. 0 of GRASPA, a benchmark to test effectiveness of grasping pipelines on physical robot setups.
Robotics
Learning latent state representation for speeding up exploration
In this work, we take a representation learning viewpoint on exploration, utilizing prior experience to learn effective latent representations, which can subsequently indicate which regions to explore.
Markerless visual servoing on unknown objects for humanoid robot platforms
To precisely reach for an object with a humanoid robot, it is of central importance to have good knowledge of both end-effector, object pose and shape.
Robotics Systems and Control Computation
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency.
