65 citations · 104 across the 9 of their papers we have counts for
9 papers
VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents
Zirui Wang, Junyi Zhang, Jiaxin Ge +9
Modern Vision-Language Models (VLMs) remain poorly characterized in multi-step visual interactions, particularly in how they integrate perception, memory, and action over long hori…
FogROS2-FT: Fault Tolerant Cloud Robotics
Kaiyuan Chen, Kush Hari, Trinity Chung +8
Cloud robotics enables robots to offload complex computational tasks to cloud servers for performance and ease of management. However, cloud compute can be costly, cloud services c…
Stylus: Automatic Adapter Selection for Diffusion Models
Michael Luo, Justin Wong, Brandon Trabucco +5
Beyond scaling base models with more data or parameters, fine-tuned adapters provide an alternative way to generate high fidelity, custom images at reduced costs. As such, adapters…
FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 2
Jeffrey Ichnowski, Kaiyuan Chen, Karthik Dharmarajan +12
Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run contemporary robot algorithms at desired rates. Cloud computing p…
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance
Michael Luo, Ashwin Balakrishna, Brijen Thananjeyan +6
Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments. Recent work learns risk measures which measure the probability of violating const…
Accelerating Quadratic Optimization with Reinforcement Learning
Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato +6
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapid…
Hierarchical Variational Imitation Learning of Control Programs
Roy Fox, Richard Shin, William Paul +5
Autonomous agents can learn by imitating teacher demonstrations of the intended behavior. Hierarchical control policies are ubiquitously useful for such learning, having the potent…
DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations
Sanjay Krishnan, Roy Fox, Ion Stoica +1
An option is a short-term skill consisting of a control policy for a specified region of the state space, and a termination condition recognizing leaving that region. In prior work…
Multi-Level Discovery of Deep Options
Roy Fox, Sanjay Krishnan, Ion Stoica +1
Augmenting an agent's control with useful higher-level behaviors called options can greatly reduce the sample complexity of reinforcement learning, but manually designing options i…