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Found 24 papers, 9 papers with code
Real-World Offline Reinforcement Learning from Vision Language Model Feedback
Our method then learns a policy using offline RL with the reward-labeled dataset.
EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography
This paper introduces the first generalization and adaptation benchmark using machine learning for evaluating out-of-distribution performance of electromyography (EMG) classification algorithms.
Towards an LLM-Based Speech Interface for Robot-Assisted Feeding
Speech interfaces, especially ones that utilize Large Language Models (LLMs), can enable individuals to effectively and naturally communicate high-level commands and nuanced preferences to robots.
VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots
Physically assistive robots present an opportunity to significantly increase the well-being and independence of individuals with motor impairments or other forms of disability who are unable to complete activities of daily living.
BodyMAP -- Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed
In contrast, we introduce BodyMAP, which jointly predicts the human body mesh and 3D applied pressure map across the entire human body.
DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning
The key to our approach is to leverage the recent progress in differentiable trajectory optimization, which enables computing the gradients of the loss with respect to the parameters of trajectory optimization.
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to design effective reward functions.
BodyMAP - Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed
In contrast we introduce BodyMAP which jointly predicts the human body mesh and 3D applied pressure map across the entire human body.
RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation.
Quantifying Assistive Robustness Via the Natural-Adversarial Frontier
We propose that capturing robustness in these interactive settings requires constructing and analyzing the entire natural-adversarial frontier: the Pareto-frontier of human policies that are the best trade-offs between naturalness and low robot performance.
A Multimodal Sensing Ring for Quantification of Scratch Intensity
In this work, we present a framework for the estimation of scratch intensity in addition to the detection of scratch.
Learning Representations that Enable Generalization in Assistive Tasks
We advocate that generalization to such OOD policies benefits from (1) learning a good latent representation for human policies that test-time humans can accurately be mapped to, and (2) making that representation adaptable with test-time interaction data, instead of relying on it to perfectly capture the space of human policies based on the simulated population only.
EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics
We study the problem of learning graph dynamics of deformable objects that generalizes to unknown physical properties.
Visual Haptic Reasoning: Estimating Contact Forces by Observing Deformable Object Interactions
Robotic manipulation of highly deformable cloth presents a promising opportunity to assist people with several daily tasks, such as washing dishes; folding laundry; or dressing, bathing, and hygiene assistance for individuals with severe motor impairments.
Causal Confusion and Reward Misidentification in Preference-Based Reward Learning
While much prior work focuses on causal confusion in reinforcement learning and behavioral cloning, we focus on a systematic study of causal confusion and reward misidentification when learning from preferences.
Assistive VR Gym: Interactions with Real People to Improve Virtual Assistive Robots
Through a formal study with eight participants in AVR Gym, we found that the Original policies performed poorly, the Revised policies performed significantly better, and that improvements to the biomechanical models used to train the Revised policies resulted in simulated people that better match real participants.
Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
Finally, we present how a robot can combine this high resolution local sensing with images from the robot's head-mounted camera to achieve accurate material classification over a scene of objects on a table.
Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
We describe a physics-based method that simulates human bodies at rest in a bed with a pressure sensing mat, and present PressurePose, a synthetic dataset with 206K pressure images with 3D human poses and shapes.
3D human pose and shape estimation
3D Human Shape Estimation
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Assistive Gym: A Physics Simulation Framework for Assistive Robotics
Assistive Gym models a person's physical capabilities and preferences for assistance, which are used to provide a reward function.
Learning to Collaborate from Simulation for Robot-Assisted Dressing
We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing.
Classification of Household Materials via Spectroscopy
To explore this, we collected a dataset of spectral measurements from two commercially available spectrometers during which a robotic platform interacted with 50 flat material objects, and we show that a neural network model can accurately analyze these measurements.
3D Human Pose Estimation on a Configurable Bed from a Pressure Image
In this work, we present two convolutional neural networks to estimate the 3D joint positions of a person in a configurable bed from a single pressure image.
Deep Haptic Model Predictive Control for Robot-Assisted Dressing
The physical implications of dressing are complicated by non-rigid garments, which can result in a robot indirectly applying high forces to a person's body.
Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks
Our approach achieves state-of-the-art results and enables a robot to estimate the material class of household objects with ~90% accuracy when 92% of the training data are unlabeled.
