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Found 8 papers, 2 papers with code
Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving
Although these models have conventionally been evaluated for open-loop prediction, we show that they can be used to parameterize autoregressive closed-loop models without retraining.
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following
Diffusion-ES samples trajectories during evolutionary search from a diffusion model and scores them using a black-box reward function.
Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation
Our aim is to devise a robotic reinforcement learning system for learning navigation and manipulation together, in an autonomous way without human intervention, enabling continual learning under realistic assumptions.
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
Despite decades of research, general purpose in-hand manipulation remains one of the unsolved challenges of robotics.
Morphology-Agnostic Visual Robotic Control
Existing approaches for visuomotor robotic control typically require characterizing the robot in advance by calibrating the camera or performing system identification.
REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning
We envision REPLAB as a framework for reproducible research across manipulation tasks, and as a step in this direction, we define a template for a grasping benchmark consisting of a task definition, evaluation protocol, performance measures, and a dataset of 92k grasp attempts.
Data-efficient Learning of Morphology and Controller for a Microrobot
Robot design is often a slow and difficult process requiring the iterative construction and testing of prototypes, with the goal of sequentially optimizing the design.
Learning Flexible and Reusable Locomotion Primitives for a Microrobot
This approach formalizes locomotion as a contextual policy search task to collect data, and subsequently uses that data to learn multi-objective locomotion primitives that can be used for planning.
