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Found 10 papers, 3 papers with code
On-Robot Reinforcement Learning with Goal-Contrastive Rewards
We find that GCR leads to a more-sample efficient RL, enabling model-free RL to solve about twice as many tasks as our baseline reward learning methods.
Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection
Automating the assembly of objects from their parts is a complex problem with innumerable applications in manufacturing, maintenance, and recycling.
Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks
Designing robotic agents to perform open vocabulary tasks has been the long-standing goal in robotics and AI.
Human-oriented Representation Learning for Robotic Manipulation
We introduce Task Fusion Decoder as a plug-and-play embedding translator that utilizes the underlying relationships among these perceptual skills to guide the representation learning towards encoding meaningful structure for what's important for all perceptual skills, ultimately empowering learning of downstream robotic manipulation tasks.
Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework
In this work, we investigate the potential of improving multi-task training and also leveraging it for transferring in the reinforcement learning setting.
PaCo: Parameter-Compositional Multi-Task Reinforcement Learning
However, the gaps between contents and difficulties of different tasks bring us challenges on both which tasks should share the parameters and what parameters should be shared, as well as the optimization challenges due to parameter sharing.
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
It is hard to replicate these approaches in trajectory forecasting due to the lack of adequate trajectory data (e. g., 34K samples in the nuScenes dataset).
Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction
To address these issues, we propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
Cross Domain Robot Imitation with Invariant Representation
In this paper, we consider CDIL on a class of similar robots.
Diverse Critical Interaction Generation for Planning and Planner Evaluation
Experiments show that our model can generate diverse interactions in various scenarios.
