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Found 14 papers, 4 papers with code
Unlocking Generalization for Robotics via Modularity and Scale
Therefore, this thesis seeks to tackle the task of building generalist robot agents by integrating these components into one: combining modularity with large-scale learning for general purpose robot control.
Local Policies Enable Zero-shot Long-horizon Manipulation
We transfer our local policies from simulation to reality and observe they can solve unseen long-horizon manipulation tasks with up to 8 stages with significant pose, object and scene configuration variation.
Neural MP: A Generalist Neural Motion Planner
We perform a thorough evaluation of our method on 64 motion planning tasks across four diverse environments with randomized poses, scenes and obstacles, in the real world, demonstrating an improvement of 23%, 17% and 79% motion planning success rate over state of the art sampling, optimization and learning based planning methods.
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks
Large Language Models (LLMs) have been shown to be capable of performing high-level planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined skill library (e. g. picking, placing, pulling, pushing, navigating).
Imitating Task and Motion Planning with Visuomotor Transformers
In this work, we show that the combination of large-scale datasets generated by TAMP supervisors and flexible Transformer models to fit them is a powerful paradigm for robot manipulation.
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency
The observations gathered by this exploration policy are labelled using 3D consistency and used to improve the perception model.
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives
An alternate but important component to consider improving is the interface of the RL algorithm with the robot.
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
If we can instead allow RL algorithms to effectively use previously collected data to aid the online learning process, such applications could be made substantially more practical: the prior data would provide a starting point that mitigates challenges due to exploration and sample complexity, while the online training enables the agent to perfect the desired skill.
Scalable Multi-Task Imitation Learning with Autonomous Improvement
In this work, we target this challenge, aiming to build an imitation learning system that can continuously improve through autonomous data collection, while simultaneously avoiding the explicit use of reinforcement learning, to maintain the stability, simplicity, and scalability of supervised imitation.
Autoregressive Models: What Are They Good For?
Autoregressive (AR) models have become a popular tool for unsupervised learning, achieving state-of-the-art log likelihood estimates.
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills.
Visual Reinforcement Learning with Imagined Goals
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires.
Composable Deep Reinforcement Learning for Robotic Manipulation
Second, we show that policies learned with soft Q-learning can be composed to create new policies, and that the optimality of the resulting policy can be bounded in terms of the divergence between the composed policies.
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
TDMs combine the benefits of model-free and model-based RL: they leverage the rich information in state transitions to learn very efficiently, while still attaining asymptotic performance that exceeds that of direct model-based RL methods.
