32 citations · 162 across the 19 of their papers we have counts for
20 papers
Dynamic Execution Horizon Prediction for Chunk-based Robot Policies
Yuchi Zhao, Miroslav Bogdanovic, Arjun Sohal +5
Action chunking has become a standard design in modern robot policies, from diffusion/flow policies to vision-language-action models, where the policy predicts a sequence of action…
Diversity You Can Actually Measure: A Fast, Model-Free Diversity Metric for Robotics Datasets
Sreevardhan Sirigiri, Nathan Samuel de Lara, Christopher Agia +2
Robotics datasets for imitation learning typically consist of long-horizon trajectories of different lengths over states, actions, and high-dimensional observations (e.g., RGB vide…
MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation
Kourosh Darvish, Arjun Sohal, Abhijoy Mandal +20
Accelerated materials discovery is critical for addressing global challenges. However, developing new laboratory workflows relies heavily on real-world experimental trials, and thi…
Informing Acquisition Functions via Foundation Models for Molecular Discovery
Qi Chen, Fabio Ramos, Alán Aspuru-Guzik +1
Bayesian Optimization (BO) is a key methodology for accelerating molecular discovery by estimating the mapping from molecules to their properties while seeking the optimal candidat…
RoboCulture: A Robotics Platform for Automated Biological Experimentation
Kevin Angers, Kourosh Darvish, Naruki Yoshikawa +6
Automating biological experimentation remains challenging due to the need for millimeter-scale precision, long and multi-step experiments, and the dynamic nature of living systems.…
AnyPlace: Learning Generalized Object Placement for Robot Manipulation
Yuchi Zhao, Miroslav Bogdanovic, Chengyuan Luo +5
Object placement in robotic tasks is inherently challenging due to the diversity of object geometries and placement configurations. To address this, we propose AnyPlace, a two-stag…
Accelerating Discovery in Natural Science Laboratories with AI and Robotics: Perspectives and Challenges from the 2024 IEEE ICRA Workshop, Yokohama, Japan
Andrew I. Cooper, Patrick Courtney, Kourosh Darvish +20
Science laboratory automation enables accelerated discovery in life sciences and materials. However, it requires interdisciplinary collaboration to address challenges such as robus…
ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization
Kourosh Darvish, Marta Skreta, Yuchi Zhao +9
Chemistry experiments can be resource- and labor-intensive, often requiring manual tasks like polishing electrodes in electrochemistry. Traditional lab automation infrastructure fa…
Errors are Useful Prompts: Instruction Guided Task Programming with Verifier-Assisted Iterative Prompting
Marta Skreta, Naruki Yoshikawa, Sebastian Arellano-Rubach +6
Generating low-level robot task plans from high-level natural language instructions remains a challenging problem. Although large language models have shown promising results in ge…
MVTrans: Multi-View Perception of Transparent Objects
Yi Ru Wang, Yuchi Zhao, Haoping Xu +4
Transparent object perception is a crucial skill for applications such as robot manipulation in household and laboratory settings. Existing methods utilize RGB-D or stereo inputs t…
Chemistry Lab Automation via Constrained Task and Motion Planning
Naruki Yoshikawa, Andrew Zou Li, Kourosh Darvish +6
Chemists need to perform many laborious and time-consuming experiments in the lab to discover and understand the properties of new materials. To support and accelerate this process…
NeurIPS 2022 Competition: Driving SMARTS
Amir Rasouli, Randy Goebel, Matthew E. Taylor +15
Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts that are prevalent in real-world autonomous dri…
Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects
Haoping Xu, Yi Ru Wang, Sagi Eppel +3
The basis of many object manipulation algorithms is RGB-D input. Yet, commodity RGB-D sensors can only provide distorted depth maps for a wide range of transparent objects due ligh…
Latent Skill Planning for Exploration and Transfer
Kevin Xie, Homanga Bharadhwaj, Danijar Hafner +2
To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge. For example, a learned world model captures knowledge about the environm…
Conservative Safety Critics for Exploration
Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart +3
Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these pol…
Making Sense of the Robotized Pandemic Response: A Comparison of Global and Canadian Robot Deployments and Success Factors
T. Barfoot, J. Burgner-Kahrs, E. Diller +12
From disinfection and remote triage, to logistics and delivery, countries around the world are making use of robots to address the unique challenges presented by the COVID-19 pande…
Continual Model-Based Reinforcement Learning with Hypernetworks
Yizhou Huang, Kevin Xie, Homanga Bharadhwaj +1
Effective planning in model-based reinforcement learning (MBRL) and model-predictive control (MPC) relies on the accuracy of the learned dynamics model. In many instances of MBRL a…
LEAF: Latent Exploration Along the Frontier
Homanga Bharadhwaj, Animesh Garg, Florian Shkurti
Self-supervised goal proposal and reaching is a key component for exploration and efficient policy learning algorithms. Such a self-supervised approach without access to any oracle…
Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization
Homanga Bharadhwaj, Kevin Xie, Florian Shkurti
Recent works in high-dimensional model-predictive control and model-based reinforcement learning with learned dynamics and reward models have resorted to population-based optimizat…
Diversity inducing Information Bottleneck in Model Ensembles
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal +3
Although deep learning models have achieved state-of-the-art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive…