3 citations · 4 across the 10 of their papers we have counts for
10 papers
Rapid co-design of Buoyancy-assisted robots for Challenging Locomotion using Gaussian Evolutionary Specialists
Ankit Sinha, Nitish Sontakke, Dennis Hong +2
Designing high-performance legged robots requires jointly optimizing morphology and control. Model-free Reinforcement Learning (RL) offers an alternative to model-predictive contro…
RobotDesignGPT: Automated Robot Design Synthesis using Vision Language Models
Nitish Sontakke, K. Niranjan Kumar, Sehoon Ha
Robot design is a nontrivial process that involves careful consideration of multiple criteria, including user specifications, kinematic structures, and visual appearance. Therefore…
Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game
Jeonghwan Kim, Wontaek Kim, Yidan Lu +9
Recent advances in whole-body robot control have enabled humanoid and legged robots to perform increasingly agile and coordinated motions. However, standardized benchmarks for eval…
Leveraging Procedural Knowledge and Task Hierarchies for Efficient Instructional Video Pre-training
Karan Samel, Nitish Sontakke, Irfan Essa
Instructional videos provide a convenient modality to learn new tasks (ex. cooking a recipe, or assembling furniture). A viewer will want to find a corresponding video that reflect…
Exploring Efficient Foundational Multi-modal Models for Video Summarization
Karan Samel, Apoorva Beedu, Nitish Sontakke +1
Foundational models are able to generate text outputs given prompt instructions and text, audio, or image inputs. Recently these models have been combined to perform tasks on video…
BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning
Tianle Huang, Nitish Sontakke, K. Niranjan Kumar +4
Domain randomization (DR), which entails training a policy with randomized dynamics, has proven to be a simple yet effective algorithm for reducing the gap between simulation and t…
Learning a Single Policy for Diverse Behaviors on a Quadrupedal Robot using Scalable Motion Imitation
Arnaud Klipfel, Nitish Sontakke, Ren Liu +1
Learning various motor skills for quadrupedal robots is a challenging problem that requires careful design of task-specific mathematical models or reward descriptions. In this work…
Residual Physics Learning and System Identification for Sim-to-real Transfer of Policies on Buoyancy Assisted Legged Robots
Nitish Sontakke, Hosik Chae, Sangjoon Lee +3
The light and soft characteristics of Buoyancy Assisted Lightweight Legged Unit (BALLU) robots have a great potential to provide intrinsically safe interactions in environments inv…
Solving Challenging Control Problems Using Two-Staged Deep Reinforcement Learning
Nitish Sontakke, Sehoon Ha
We present a deep reinforcement learning (deep RL) algorithm that consists of learning-based motion planning and imitation to tackle challenging control problems. Deep RL has been…
PM-FSM: Policies Modulating Finite State Machine for Robust Quadrupedal Locomotion
Ren Liu, Nitish Sontakke, Sehoon Ha
Deep reinforcement learning (deep RL) has emerged as an effective tool for developing controllers for legged robots. However, vanilla deep RL often requires a tremendous amount of…