62 citations · 280 across the 51 of their papers we have counts for
51 papers · 1 filter
DITTO-X: Forward and Reverse Teleoperation for Dexterous Manipulation and Human Intervention
Zhanpeng He, Joaquin Palacios, Zhangyu Wang +5
Teleoperated demonstrations are a primary source of data for robot manipulation, and teleoperated interventions are a primary mechanism for correcting policies at deployment. Yet m…
ReactEMG Stroke: Healthy-to-Stroke Few-shot Adaptation for sEMG-Based Intent Detection
Runsheng Wang, Katelyn Lee, Xinyue Zhu +4
Surface electromyography (sEMG) is a promising control signal for assist-as-needed hand rehabilitation after stroke, but detecting intent from paretic muscles often requires length…
SpikeATac: A Multimodal Tactile Finger with Taxelized Dynamic Sensing for Dexterous Manipulation
Eric T. Chang, Peter Ballentine, Zhanpeng He +8
In this work, we introduce SpikeATac, a multimodal tactile finger combining a taxelized and highly sensitive dynamic response (PVDF) with a static transduction method (capacitive)…
MiniBEE: A New Form Factor for Compact Bimanual Dexterity
Sharfin Islam, Zewen Chen, Zhanpeng He +8
Bimanual robot manipulators can achieve impressive dexterity, but typically rely on two full six- or seven- degree-of-freedom arms so that paired grippers can coordinate effectivel…
A roadmap for AI in robotics
Aude Billard, Alin Albu-Schaeffer, Michael Beetz +8
AI technologies, including deep learning, large-language models have gone from one breakthrough to the other. As a result, we are witnessing growing excitement in robotics at the p…
ReactEMG: Stable, Low-Latency Intent Detection from sEMG via Masked Modeling
Runsheng Wang, Xinyue Zhu, Ava Chen +5
Surface electromyography (sEMG) signals show promise for effective human-machine interfaces, particularly in rehabilitation and prosthetics. However, challenges remain in developin…
Train Robots in a JIF: Joint Inverse and Forward Dynamics with Human and Robot Demonstrations
Gagan Khandate, Boxuan Wang, Sarah Park +7
Pre-training on large datasets of robot demonstrations is a powerful technique for learning diverse manipulation skills but is often limited by the high cost and complexity of coll…
VibeCheck: Using Active Acoustic Tactile Sensing for Contact-Rich Manipulation
Kaidi Zhang, Do-Gon Kim, Eric T. Chang +6
The acoustic response of an object can reveal a lot about its global state, for example its material properties or the extrinsic contacts it is making with the world. In this work,…
Reciprocal Learning of Intent Inferral with Augmented Visual Feedback for Stroke
Jingxi Xu, Ava Chen, Lauren Winterbottom +5
Intent inferral, the process by which a robotic device predicts a user's intent from biosignals, offers an effective and intuitive way to control wearable robots. Classical intent…
Compact LED-Based Displacement Sensing for Robot Fingers
Amr El-Azizi, Sharfin Islam, Pedro Piacenza +3
In this paper, we introduce a sensor designed for integration in robot fingers, where it can provide information on the displacements induced by external contact. Our sensor uses L…
ChatEMG: Synthetic Data Generation to Control a Robotic Hand Orthosis for Stroke
Jingxi Xu, Runsheng Wang, Siqi Shang +10
Intent inferral on a hand orthosis for stroke patients is challenging due to the difficulty of data collection. Additionally, EMG signals exhibit significant variations across diff…
Task-Based Design and Policy Co-Optimization for Tendon-driven Underactuated Kinematic Chains
Sharfin Islam, Zhanpeng He, Matei Ciocarlie
Underactuated manipulators reduce the number of bulky motors, thereby enabling compact and mechanically robust designs. However, fewer actuators than joints means that the manipula…
Meta-Learning for Fast Adaptation in Intent Inferral on a Robotic Hand Orthosis for Stroke
Pedro Leandro La Rotta, Jingxi Xu, Ava Chen +5
We propose MetaEMG, a meta-learning approach for fast adaptation in intent inferral on a robotic hand orthosis for stroke. One key challenge in machine learning for assistive and r…
Grasp Force Assistance via Throttle-based Wrist Angle Control on a Robotic Hand Orthosis for C6-C7 Spinal Cord Injury
Joaquin Palacios, Alexandra Deli-Ivanov, Ava Chen +4
Individuals with hand paralysis resulting from C6-C7 spinal cord injuries frequently rely on tenodesis for grasping. However, tenodesis generates limited grasping force and demands…
Volitional Control of the Paretic Hand Post-Stroke Increases Finger Stiffness and Resistance to Robot-Assisted Movement
Ava Chen, Katelyn Lee, Lauren Winterbottom +7
Increased effort during use of the paretic arm and hand can provoke involuntary abnormal synergy patterns and amplify stiffness effects of muscle tone for individuals after stroke,…
Tactile-based Object Retrieval From Granular Media
Jingxi Xu, Yinsen Jia, Dongxiao Yang +5
We introduce GEOTACT, the first robotic system capable of grasping and retrieving objects of potentially unknown shapes buried in a granular environment. While important in many ap…
RR: Rapid eXploration for Reinforcement Learning via Sampling-based Reset Distributions and Imitation Pre-training
Gagan Khandate, Tristan L. Saidi, Siqi Shang +5
We present a method for enabling Reinforcement Learning of motor control policies for complex skills such as dexterous manipulation. We posit that a key difficulty for training suc…
Towards Tenodesis-Modulated Control of an Assistive Hand Exoskeleton for SCI
Joaquin Palacios, Alexandra Deli-Ivanov, Ava Chen +4
Restoration of hand function is one of the highest priorities for SCI populations. In this work, we present a prototype of a robotic assistive orthosis capable of implementing teno…
An Investigation of Multi-feature Extraction and Super-resolution with Fast Microphone Arrays
Eric T. Chang, Runsheng Wang, Peter Ballentine +5
In this work, we use MEMS microphones as vibration sensors to simultaneously classify texture and estimate contact position and velocity. Vibration sensors are an important facet o…
MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy
Zhanpeng He, Matei Ciocarlie
We introduce MORPH, a method for co-optimization of hardware design parameters and control policies in simulation using reinforcement learning. Like most co-optimization methods, M…
Decision Making for Human-in-the-loop Robotic Agents via Uncertainty-Aware Reinforcement Learning
Siddharth Singi, Zhanpeng He, Alvin Pan +5
In a Human-in-the-Loop paradigm, a robotic agent is able to act mostly autonomously in solving a task, but can request help from an external expert when needed. However, knowing wh…
Dexterous In-hand Manipulation by Guiding Exploration with Simple Sub-skill Controllers
Gagan Khandate, Cameron Mehlman, Xingsheng Wei +1
Recently, reinforcement learning has led to dexterous manipulation skills of increasing complexity. Nonetheless, learning these skills in simulation still exhibits poor sample-effi…
Sampling-based Exploration for Reinforcement Learning of Dexterous Manipulation
Gagan Khandate, Siqi Shang, Eric T. Chang +5
In this paper, we present a novel method for achieving dexterous manipulation of complex objects, while simultaneously securing the object without the use of passive support surfac…
TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors
Jingxi Xu, Shuran Song, Matei Ciocarlie
Inspired by the human ability to perform complex manipulation in the complete absence of vision (like retrieving an object from a pocket), the robotic manipulation field is motivat…