Ajinkya Jain

Hi there! I am a robotics researcher at Intrinsic.ai (an Alphabet company), where I am a part of the R&D team led by Prof. Stefan Schaal. My research lies at the intersection of artificial intelligence (AI) and machine learning (ML) with robot motion planning, dynamics, and control. I am particularly interested in developing methods that enable robots to perform dexterous manipulation tasks with robustness in the face of uncertainty.

Before joining Intrinsic, I obtained my Ph.D. in robotics from the University of Texas at Austin under the guidance of Prof. Scott Niekum and Prof. Ashish Deshpande . My doctoral research focused on two key areas: developing learning algorithms for robots to acquire object interaction models from visual data and motion planners to perform robot manipulation tasks robustly under uncertainty. Prior to joining UT Austin, I obtained my Bachelors and Masters in Mechanical Engineering from IIT Kanpur.

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Research

I'm interested in robot learning, dexterous robot manipulation, motion planning under uncertainty, model learning for planning and control, reinforcement learning, and optimal control.

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Large-scale datasets in standardized data formats and models to enable learning “generalist” X-robot policy that can be adapted efficiently to new robots, tasks, and environments.

Efficient Online Learning of Contact Force Models for Connector Insertion

An efficient and scalable approach for modeling connector insertion environments by learning a quasi-static contact force model instead of a full simulator, employing a Linear Model Learning algorithm for real-time optimal mapping without matrix inversions.

Distributional Depth-Based Estimation of Object Articulation Models

A novel method that efficiently learns distributions over articulation model parameters directly from depth images without the need to know articulation model categories a priori

ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory

A novel method for estimating articulation models for objects directly from raw depth images without knowing their articulation type a priori using screw theory

Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty

A method for learning planning-compatible hybrid kinematics models for articulated objectsfrom human demonstrations

Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics

A POMDP-based Motion planner that generates efficient motion plans leveraging object-object interactions to perform manipulation tasks under uncertainty with high success rates

Belief Space Planning under Approximate Hybrid Dynamics

Extending Belief-space LQR to hybrid systems for robot motion planning under uncertainty

A piezoelectric model based multi-objective optimization of robot gripper design

Multi-objective design optimization of a piezoelectric actuator driven gripper

Unified Minimalistic Modelling of Piezoelectric Stack Actuators for Engineering Applications

A simple, computationally-cheap, yet effective model for piezoelectric stack actuators as a replacement of black-box models used in engineering design optimization problems.

Design and source code from Jon Barron's website. Jekyll version from Leonid Keselman's website