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Found 17 papers, 6 papers with code
Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots
We present Habitat 3. 0: a simulation platform for studying collaborative human-robot tasks in home environments.
Skill Transformer: A Monolithic Policy for Mobile Manipulation
We present Skill Transformer, an approach for solving long-horizon robotic tasks by combining conditional sequence modeling and skill modularity.
Adaptive Coordination in Social Embodied Rearrangement
We present the task of "Social Rearrangement", consisting of cooperative everyday tasks like setting up the dinner table, tidying a house or unpacking groceries in a simulated multi-agent environment.
EgoTV: Egocentric Task Verification from Natural Language Task Descriptions
The goal in EgoTV is to verify the execution of tasks from egocentric videos based on the natural language description of these tasks.
Cross-Domain Transfer via Semantic Skill Imitation
We propose an approach for semantic imitation, which uses demonstrations from a source domain, e. g. human videos, to accelerate reinforcement learning (RL) in a different target domain, e. g. a robotic manipulator in a simulated kitchen.
Transformers are Adaptable Task Planners
Every home is different, and every person likes things done in their particular way.
Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots
We develop and demonstrate one of the first collaborative autonomy framework that is able to move a cable-towed load, which is too heavy to move by a single robot, through narrow spaces with real-time feedback and reactive planning in experiments.
Learning Torque Control for Quadrupedal Locomotion
Reinforcement learning (RL) has become a promising approach to developing controllers for quadrupedal robots.
Efficient and Interpretable Robot Manipulation with Graph Neural Networks
Manipulation tasks, like loading a dishwasher, can be seen as a sequence of spatial constraints and relationships between different objects.
Model-Based Inverse Reinforcement Learning from Visual Demonstrations
Scaling model-based inverse reinforcement learning (IRL) to real robotic manipulation tasks with unknown dynamics remains an open problem.
Learning State-Dependent Losses for Inverse Dynamics Learning
In both settings, the structured and state-dependent learned losses improve online adaptation speed, when compared to standard, state-independent loss functions.
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
We show empirically that properly addressing these issues significantly improves the efficacy of linear embeddings for BO on a range of problems, including learning a gait policy for robot locomotion.
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm
The recursive Newton-Euler Algorithm (RNEA) is a popular technique for computing the dynamics of robots.
Robotics
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
We propose a model and architecture for a sequential variational autoencoder that embeds the space of simulated trajectories into a lower-dimensional space of latent paths in an unsupervised way.
Curious iLQR: Resolving Uncertainty in Model-based RL
In this work, we propose a model-based reinforcement learning (MBRL) framework that combines Bayesian modeling of the system dynamics with curious iLQR, an iterative LQR approach that considers model uncertainty.
Model-based Reinforcement Learning
reinforcement-learning
+1
Deep Kernels for Optimizing Locomotion Controllers
First, we demonstrate improvement in sample efficiency when optimizing a 5-dimensional controller on the ATRIAS robot hardware.
Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion
We develop a distance metric for bipedal locomotion that enhances the sample-efficiency of Bayesian Optimization and use it to train a 16 dimensional neuromuscular model for planar walking.
