5 citations · 17 across the 31 of their papers we have counts for
21 papers · 1 filter
Robot Learning with Super-Linear Scaling
Marcel Torne, Arhan Jain, Jiayi Yuan +5
Scaling robot learning requires data collection pipelines that scale favorably with human effort. In this work, we propose Crowdsourcing and Amortizing Human Effort for Real-to-Sim…
DexHub and DART: Towards Internet Scale Robot Data Collection
Younghyo Park, Jagdeep Singh Bhatia, Lars Ankile +1
The quest to build a generalist robotic system is impeded by the scarcity of diverse and high-quality data. While real-world data collection effort exist, requirements for robot ha…
Learning to Look Around: Enhancing Teleoperation and Learning with a Human-like Actuated Neck
Bipasha Sen, Michelle Wang, Nandini Thakur +2
We introduce a teleoperation system that integrates a 5 DOF actuated neck, designed to replicate natural human head movements and perception. By enabling behaviors like peeking or…
Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification
Jaedong Hwang, Brian Cheung, Zhang-Wei Hong +3
Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of maintaining the general performance of the mo…
ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization
Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano +1
Reward shaping is critical in reinforcement learning (RL), particularly for complex tasks where sparse rewards can hinder learning. However, choosing effective shaping rewards from…
Diffusion Policy Policy Optimization
Allen Z. Ren, Justin Lidard, Lars L. Ankile +6
We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework including best practices for fine-tuning diffusion-based policies (e.g. Diffusion Policy) in conti…
EyeSight Hand: Design of a Fully-Actuated Dexterous Robot Hand with Integrated Vision-Based Tactile Sensors and Compliant Actuation
Branden Romero, Hao-Shu Fang, Pulkit Agrawal +1
In this work, we introduce the EyeSight Hand, a novel 7 degrees of freedom (DoF) humanoid hand featuring integrated vision-based tactile sensors tailored for enhanced whole-hand ma…
Everyday Finger: A Robotic Finger that Meets the Needs of Everyday Interactive Manipulation
Rubén Castro Ornelas, Tomás Cantú, Isabel Sperandio +2
We provide the mechanical and dynamical requirements for a robotic finger capable of performing thirty diverse everyday tasks. To match these requirements, we present a finger desi…
Automatic Environment Shaping is the Next Frontier in RL
Younghyo Park, Gabriel B. Margolis, Pulkit Agrawal
Many roboticists dream of presenting a robot with a task in the evening and returning the next morning to find the robot capable of solving the task. What is preventing us from ach…
Vegetable Peeling: A Case Study in Constrained Dexterous Manipulation
Tao Chen, Eric Cousineau, Naveen Kuppuswamy +1
Recent studies have made significant progress in addressing dexterous manipulation problems, particularly in in-hand object reorientation. However, there are few existing works tha…
ROER: Regularized Optimal Experience Replay
Changling Li, Zhang-Wei Hong, Pulkit Agrawal +2
Experience replay serves as a key component in the success of online reinforcement learning (RL). Prioritized experience replay (PER) reweights experiences by the temporal differen…
From Imitation to Refinement -- Residual RL for Precise Assembly
Lars Ankile, Anthony Simeonov, Idan Shenfeld +2
Recent advances in Behavior Cloning (BC) have made it easy to teach robots new tasks. However, we find that the ease of teaching comes at the cost of unreliable performance that sa…
Random Latent Exploration for Deep Reinforcement Learning
Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari +2
We introduce Random Latent Exploration (RLE), a simple yet effective exploration strategy in reinforcement learning (RL). On average, RLE outperforms noise-based methods, which per…
Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient
Zechu Li, Rickmer Krohn, Tao Chen +3
Deep reinforcement learning (RL) algorithms typically parameterize the policy as a deep network that outputs either a deterministic action or a stochastic one modeled as a Gaussian…
Learning Force Control for Legged Manipulation
Tifanny Portela, Gabriel B. Margolis, Yandong Ji +1
Controlling contact forces during interactions is critical for locomotion and manipulation tasks. While sim-to-real reinforcement learning (RL) has succeeded in many contact-rich p…
Value Augmented Sampling for Language Model Alignment and Personalization
Seungwook Han, Idan Shenfeld, Akash Srivastava +2
Aligning Large Language Models (LLMs) to cater to different human preferences, learning new skills, and unlearning harmful behavior is an important problem. Search-based methods, s…
Rank2Reward: Learning Shaped Reward Functions from Passive Video
Daniel Yang, Davin Tjia, Jacob Berg +3
Teaching robots novel skills with demonstrations via human-in-the-loop data collection techniques like kinesthetic teaching or teleoperation puts a heavy burden on human supervisor…
JUICER: Data-Efficient Imitation Learning for Robotic Assembly
Lars Ankile, Anthony Simeonov, Idan Shenfeld +1
While learning from demonstrations is powerful for acquiring visuomotor policies, high-performance imitation without large demonstration datasets remains challenging for tasks requ…
Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
Marcel Torne, Anthony Simeonov, Zechu Li +4
Imitation learning methods need significant human supervision to learn policies robust to changes in object poses, physical disturbances, and visual distractors. Reinforcement lear…
Curiosity-driven Red-teaming for Large Language Models
Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang +5
Large language models (LLMs) hold great potential for many natural language applications but risk generating incorrect or toxic content. To probe when an LLM generates unwanted con…
Training Neural Networks from Scratch with Parallel Low-Rank Adapters
Minyoung Huh, Brian Cheung, Jeremy Bernstein +2
The scalability of deep learning models is fundamentally limited by computing resources, memory, and communication. Although methods like low-rank adaptation (LoRA) have reduced th…