78 citations · 196 across the 72 of their papers we have counts for
21 papers · 1 filter
FORM: Robot Manipulation through Direct Material Law Identification
Stepan Tretiakov, Ruihan Zhao, Cheng-Hsi Hsiao +6
When interacting with an unfamiliar deformable material, a robot lacks prior knowledge of its physical properties and how it will respond to applied forces and motion. Rapid online…
RoboShape: Information-Theoretic Point Cloud Representations for Privacy-Aware Robot Perception
Oguzhan Baser, Mirac Sozen, Kaan Kale +2
With the increased adoption of robotic agents operating in human environments by scanning and sharing 3D representations (e.g., for fleet learning, cloud-based planning, or collabo…
MoS-VLA: A Vision-Language-Action Model with One-Shot Skill Adaptation
Ruihan Zhao, Tyler Ingebrand, Sandeep Chinchali +1
Vision-Language-Action (VLA) models trained on large robot datasets promise general-purpose, robust control across diverse domains and embodiments. However, existing approaches oft…
UNCAP: Uncertainty-Guided Neurosymbolic Planning Using Natural Language Communication for Cooperative Autonomous Vehicles
Neel P. Bhatt, Po-han Li, Kushagra Gupta +7
Safe large-scale coordination of multiple cooperative connected autonomous vehicles (CAVs) hinges on communication that is both efficient and interpretable. Existing approaches eit…
IG-MCTS: Human-in-the-Loop Cooperative Navigation under Incomplete Information
Shenghui Chen, Ruihan Zhao, Sandeep Chinchali +1
Human-robot cooperative navigation is challenging under incomplete information. We introduce CoNav-Maze, a simulated environment where a robot navigates with local perception while…
PEERNet: An End-to-End Profiling Tool for Real-Time Networked Robotic Systems
Aditya Narayanan, Pranav Kasibhatla, Minkyu Choi +3
Networked robotic systems balance compute, power, and latency constraints in applications such as self-driving vehicles, drone swarms, and teleoperated surgery. A core problem in t…
Robot-Enabled Machine Learning-Based Diagnosis of Gastric Cancer Polyps Using Partial Surface Tactile Imaging
Siddhartha Kapuria, Jeff Bonyun, Yash Kulkarni +3
In this paper, to collectively address the existing limitations on endoscopic diagnosis of Advanced Gastric Cancer (AGC) Tumors, for the first time, we propose (i) utilization and…
Online Foundation Model Selection in Robotics
Po-han Li, Oyku Selin Toprak, Aditya Narayanan +2
Foundation models have recently expanded into robotics after excelling in computer vision and natural language processing. The models are accessible in two ways: open-source or pai…
Using human and robot synthetic data for training smart hand tools
Jose Bendana, Sundar Sripada V. S., Carlos D. Salazar +2
The future of work does not require a choice between human and robot. Aside from explicit human-robot collaboration, robotics can play an increasingly important role in helping tra…
ALT-Pilot: Autonomous navigation with Language augmented Topometric maps
Mohammad Omama, Pranav Inani, Pranjal Paul +4
We present an autonomous navigation system that operates without assuming HD LiDAR maps of the environment. Our system, ALT-Pilot, relies only on publicly available road network in…
Learning Sparse Control Tasks from Pixels by Latent Nearest-Neighbor-Guided Explorations
Ruihan Zhao, Ufuk Topcu, Sandeep Chinchali +1
Recent progress in deep reinforcement learning (RL) and computer vision enables artificial agents to solve complex tasks, including locomotion, manipulation and video games from hi…
Safe Networked Robotics with Probabilistic Verification
Sai Shankar Narasimhan, Sharachchandra Bhat, Sandeep P. Chinchali
Autonomous robots must utilize rich sensory data to make safe control decisions. To process this data, compute-constrained robots often require assistance from remote computation,…
Robust Forecasting for Robotic Control: A Game-Theoretic Approach
Shubhankar Agarwal, David Fridovich-Keil, Sandeep P. Chinchali
Modern robots require accurate forecasts to make optimal decisions in the real world. For example, self-driving cars need an accurate forecast of other agents' future actions to pl…
Dynamic Selection of Perception Models for Robotic Control
Bineet Ghosh, Masaad Khan, Adithya Ashok +2
Robotic perception models, such as Deep Neural Networks (DNNs), are becoming more computationally intensive and there are several models being trained with accuracy and latency tra…
Synthesizing Adversarial Visual Scenarios for Model-Based Robotic Control
Shubhankar Agarwal, Sandeep P. Chinchali
Today's robots often interface with data-driven perception and planning models with classical model-predictive controllers (MPC). Often, such learned perception/planning models pro…
Drift Reduced Navigation with Deep Explainable Features
Mohd Omama, Sundar Sripada Venugopalaswamy Sriraman, Sandeep Chinchali +2
Modern autonomous vehicles (AVs) often rely on vision, LIDAR, and even radar-based simultaneous localization and mapping (SLAM) frameworks for precise localization and navigation.…
Learning Actions for Drift-Free Navigation in Highly Dynamic Scenes
Mohd Omama, Sundar Sripada V. S., Sandeep Chinchali +1
We embark on a hitherto unreported problem of an autonomous robot (self-driving car) navigating in dynamic scenes in a manner that reduces its localization error and eventual cumul…
Interpretable Trade-offs Between Robot Task Accuracy and Compute Efficiency
Bineet Ghosh, Sandeep Chinchali, Parasara Sridhar Duggirala
A robot can invoke heterogeneous computation resources such as CPUs, cloud GPU servers, or even human computation for achieving a high-level goal. The problem of invoking an approp…
Sampling Training Data for Continual Learning Between Robots and the Cloud
Sandeep Chinchali, Evgenya Pergament, Manabu Nakanoya +5
Today's robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data, such as rare scenes of road constructi…
Task-relevant Representation Learning for Networked Robotic Perception
Manabu Nakanoya, Sandeep Chinchali, Alexandros Anemogiannis +3
Today, even the most compute-and-power constrained robots can measure complex, high data-rate video and LIDAR sensory streams. Often, such robots, ranging from low-power drones to…
Network Offloading Policies for Cloud Robotics: a Learning-based Approach
Sandeep Chinchali, Apoorva Sharma, James Harrison +6
Today's robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and obje…