9 citations · 14 across the 13 of their papers we have counts for
14 papers
Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?
Tyler Ga Wei Lum, Kushal Kedia, C. Karen Liu +1
Multi-fingered robots promise the speed and dexterity of human hands, yet challenging problems such as precise assembly have remained out of reach. These tasks are contact-rich, ma…
SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation
Kushal Kedia, Tyler Ga Wei Lum, Jeannette Bohg +1
The ability to manipulate tools significantly expands the set of tasks a robot can perform. Yet, tool manipulation represents a challenging class of dexterity, requiring grasping t…
X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations
Maximus A. Pace, Prithwish Dan, Chuanruo Ning +5
Human videos are a scalable source of training data for robot learning. However, humans and robots significantly differ in embodiment, making many human actions infeasible for dire…
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real
Prithwish Dan, Kushal Kedia, Angela Chao +4
Human videos offer a scalable way to train robot manipulation policies, but lack the action labels needed by standard imitation learning algorithms. Existing cross-embodiment appro…
Query-Efficient Planning with Language Models
Gonzalo Gonzalez-Pumariega, Wayne Chen, Kushal Kedia +1
Planning in complex environments requires an agent to efficiently query a world model to find a feasible sequence of actions from start to goal. Recent work has shown that Large La…
One-Shot Imitation under Mismatched Execution
Kushal Kedia, Prithwish Dan, Angela Chao +2
Human demonstrations as prompts are a powerful way to program robots to do long-horizon manipulation tasks. However, translating these demonstrations into robot-executable actions…
MOSAIC: Modular Foundation Models for Assistive and Interactive Cooking
Huaxiaoyue Wang, Kushal Kedia, Juntao Ren +14
We present MOSAIC, a modular architecture for coordinating multiple robots to (a) interact with users using natural language and (b) manipulate an open vocabulary of everyday objec…
ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting
Kushal Kedia, Prithwish Dan, Atiksh Bhardwaj +1
Seamless human-robot manipulation in close proximity relies on accurate forecasts of human motion. While there has been significant progress in learning forecast models at scale, w…
InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions
Kushal Kedia, Atiksh Bhardwaj, Prithwish Dan +1
In collaborative human-robot manipulation, a robot must predict human intents and adapt its actions accordingly to smoothly execute tasks. However, the human's intent in turn depen…
A Game-Theoretic Framework for Joint Forecasting and Planning
Kushal Kedia, Prithwish Dan, Sanjiban Choudhury
Planning safe robot motions in the presence of humans requires reliable forecasts of future human motion. However, simply predicting the most likely motion from prior interactions…
Rationale-Guided Few-Shot Classification to Detect Abusive Language
Punyajoy Saha, Divyanshu Sheth, Kushal Kedia +2
Abusive language is a concerning problem in online social media. Past research on detecting abusive language covers different platforms, languages, demographies, etc. However, mode…
Optimal Multi-Agent Path Finding for Precedence Constrained Planning Tasks
Kushal Kedia, Rajat Kumar Jenamani, Aritra Hazra +1
Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents from their start locations to end locations. We consider an extension to this pro…
indicnlp@kgp at DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Languages
Kushal Kedia, Abhilash Nandy
The paper presents the submission of the team indicnlp@kgp to the EACL 2021 shared task "Offensive Language Identification in Dravidian Languages." The task aimed to classify diffe…
Robotic Motion Planning using Learned Critical Sources and Local Sampling
Rajat Kumar Jenamani, Rahul Kumar, Parth Mall +1
Sampling based methods are widely used for robotic motion planning. Traditionally, these samples are drawn from probabilistic ( or deterministic ) distributions to cover the state…