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Found 12 papers, 9 papers with code
Learning Visual Quadrupedal Loco-Manipulation from Demonstrations
Our approach is validated through simulations and real-world experiments, demonstrating the robot's ability to perform tasks that demand mobility and high precision, such as lifting a basket from the ground while moving, closing a dishwasher, pressing a button, and pushing a door.
3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations
Imitation learning provides an efficient way to teach robots dexterous skills; however, learning complex skills robustly and generalizablely usually consumes large amounts of human demonstrations.
Generalizable Visual Reinforcement Learning with Segment Anything Model
Learning policies that can generalize to unseen environments is a fundamental challenge in visual reinforcement learning (RL).
Diffusion Reward: Learning Rewards via Conditional Video Diffusion
Learning rewards from expert videos offers an affordable and effective solution to specify the intended behaviors for reinforcement learning tasks.
Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected datasets.
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network.
GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
To incorporate semantics in 3D, the reconstruction module utilizes a vision-language foundation model ($\textit{e. g.}$, Stable Diffusion) to distill rich semantic information into the deep 3D voxel.
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning
Communication lays the foundation for cooperation in human society and in multi-agent reinforcement learning (MARL).
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
In this paper, we examine the effectiveness of pre-training for visuo-motor control tasks.
Visual Reinforcement Learning with Self-Supervised 3D Representations
A prominent approach to visual Reinforcement Learning (RL) is to learn an internal state representation using self-supervised methods, which has the potential benefit of improved sample-efficiency and generalization through additional learning signal and inductive biases.
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization
Temporal difference (TD) learning is a widely used method to evaluate policies in reinforcement learning.
UKPGAN: A General Self-Supervised Keypoint Detector
Keypoint detection is an essential component for the object registration and alignment.
