Adithyavairavan Murali — accepted AI-conference papers

2026

Grasp-MPC: Closed-Loop Visual Grasping Via Value-Guided Model Predictive Control

Jun Yamada, Adithyavairavan Murali, Ajay Uday Mandlekar, Clemens Eppner, Ingmar Posner, Balakumar Sundaralingam

ICRA 2026poster

Grasping of diverse objects in unstructured environments remains a significant challenge. Open-loop grasping methods, effective in controlled settings, struggle in cluttered environments. Grasp prediction errors and object pose changes during grasping are the main causes of failure. In contrast, clo…

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2026

GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping

Beining Han, Yu-Wei Chao, Erwin Coumans, Clemens Eppner, Jia Deng, Stan Birchfield +1

CVPR 2026

We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to novel gripper morphologies and physical grasping processes. Our method extends diffusion model based generative 6-DOF grasping models to condition on

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2026

GraspGen: A Diffusion-Based Framework for 6-DOF Grasping with On-Generator Training

Adithyavairavan Murali, Balakumar Sundaralingam, Yu-Wei Chao, Wentao Yuan, Jun Yamada, Mark Carlson +4

ICRA 2026poster

Grasping is a fundamental robot skill, yet despite significant research advancements, learning-based 6-DOF grasping approaches are still not turnkey and struggle to generalize across different embodiments and in-the-wild settings. We build upon the recent success on modeling the object-centric grasp…

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2026

SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL

Siyi Chen, Mikaela Angelina Uy, Chan Hee Song, Faisal Ladhak, Adithyavairavan Murali, Qing Qu +3

CVPR 2026

Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required for embodied applications. The agentic paradigm promises that VLMs can use a wide variety of tools that could augment these capabilities, such as depth e

Cited by 0 Source ↗code ↗Scholar ↗

2024

DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning

Huang Huang, Balakumar Sundaralingam, Arsalan Mousavian, Adithyavairavan Murali, Ken Goldberg, Dieter Fox

CoRL 2024poster

Running optimization across many parallel seeds leveraging GPU compute [2] have relaxed the need for a good initialization, but this can fail if the problem is highly non-convex as all seeds could get stuck in local minima. One such setting is collision-free motion optimization for robot manipulatio…

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2024

RoboPoint: A Vision-Language Model for Spatial Affordance Prediction in Robotics

Wentao Yuan, Jiafei Duan, Valts Blukis, Wilbert Pumacay, Ranjay Krishna, Adithyavairavan Murali +2

CoRL 2024poster

From rearranging objects on a table to putting groceries into shelves, robots must plan precise action points to perform tasks accurately and reliably. In spite of the recent adoption of vision language models (VLMs) to control robot behavior, VLMs struggle to precisely articulate robot actions usin…

Cited by 53 Source ↗code ↗Scholar ↗

2023

CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation

Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner, Adam Fishman, Dieter Fox

ICRA 2023poster

We address the important problem of generalizing robotic rearrangement to clutter without any explicit object models. We first generate over 650K cluttered scenes-orders of magnitude more than prior work-in diverse everyday environments, such as cabinets and shelves. We render synthetic partial poin…

Cited by 27 Source ↗code ↗Scholar ↗

2023

M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place

Wentao Yuan, Adithyavairavan Murali, Arsalan Mousavian, Dieter Fox

CoRL 2023poster

With the advent of large language models and large-scale robotic datasets, there has been tremendous progress in high-level decision-making for object manipulation. These generic models are able to interpret complex tasks using language commands, but they often have difficulties generalizing to out-…

Cited by 23 Source ↗code ↗Scholar ↗

2022

HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers

Yu-Wei Chao, Chris Paxton, Yu Xiang, Wei Yang, Balakumar Sundaralingam, Tao Chen +3

ICRA 2022poster

We introduce a new simulation benchmark “Han-doverSim” for human-to-robot object handovers. To simulate the giver's motion, we leverage a recent motion capture dataset of hand grasping of objects. We create training and evaluation environments for the receiver with standardized protocols and metrics…

Cited by 29 Source ↗code ↗Scholar ↗

2022

Motion Policy Networks

Adam Fishman, Adithyavairavan Murali, Clemens Eppner, Bryan Peele, Byron Boots, Dieter Fox

CoRL 2022poster

Collision-free motion generation in unknown environments is a core building block for robot manipulation. Generating such motions is challenging due to multiple objectives; not only should the solutions be optimal, the motion generator itself must be fast enough for real-time performance and reliab…

Cited by 66 Source ↗code ↗Scholar ↗

2020

6-DOF Grasping for Target-driven Object Manipulation in Clutter

Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner, Chris Paxton, Dieter Fox

ICRA 2020poster

Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires both reasoning about unseen object parts and potential collisions with the manipulator. Most existing data-driven approaches avoid this problem by limiting themselves to top-down planar grasps which is ins…

Cited by 263 Source ↗code ↗Scholar ↗

2020

Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping

Adithyavairavan Murali, Weiyu Liu, Kenneth Marino, Sonia Chernova, Abhinav Gupta

CoRL 2020

Despite the enormous progress and generalization in robotic grasping in recent years, existing methods have yet to scale and generalize task-oriented grasping to the same extent. This is largely due to the scale of the datasets both in terms of the number of objects and tasks studied. We address the

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2018

CASSL: Curriculum Accelerated Self-Supervised Learning

Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi, Abhinav Gupta

ICRA 2018poster

Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framework for higher-dimensional control requires either scaling up the data collection efforts or using a clever sampling strat…

Cited by 41 Source ↗Scholar ↗

2018

Hardware Conditioned Policies for Multi-Robot Transfer Learning

Tao Chen, Adithyavairavan Murali, Abhinav Gupta

NeurIPS 2018poster

Deep reinforcement learning could be used to learn dexterous robotic policies but it is challenging to transfer them to new robots with vastly different hardware properties. It is also prohibitively expensive to learn a new policy from scratch for each robot hardware due to the high sample complexit…

Cited by 108 Source ↗PDF ↗code ↗Scholar ↗

2018

Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias

Abhinav Gupta, Adithyavairavan Murali, Dhiraj Prakashchand Gandhi, Lerrel Pinto

NeurIPS 2018poster

Data-driven approaches to solving robotic tasks have gained a lot of traction in recent years. However, most existing policies are trained on large-scale datasets collected in curated lab settings. If we aim to deploy these models in unstructured visual environments like people's homes, they will be…

Cited by 167 Source ↗PDF ↗Scholar ↗

2016

TSC-DL: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with Deep Learning

Adithyavairavan Murali, Animesh Garg, Sanjay Krishnan, Florian T. Pokorny, Pieter Abbeel, Trevor Darrell +1

ICRA 2016

The growth of robot-assisted minimally invasive surgery has led to sizable datasets of fixed-camera video and kinematic recordings of surgical subtasks. Segmentation of these trajectories into locally-similar contiguous sections can facilitate learning from demonstrations, skill assessment, and salv

Cited by 77 Source ↗code ↗Scholar ↗

2015

A paced shared-control teleoperated architecture for supervised automation of multilateral surgical tasks

Kamran Shamaei, Yuhang Che, Adithyavairavan Murali, Siddarth Sen, Sachin Patil, Ken Goldberg +1

IROS 2015poster

Automation of repetitive tasks can improve laparoscopic surgical procedures by unloading surgeons and reducing duration, trauma, and expense. However, surgical procedures involve delicate manipulation of deformable tissues in a very dynamic environment, suggesting that automated execution of surgica…

Cited by 31 Source ↗Scholar ↗

2015

Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms

Adithyavairavan Murali, Siddarth Sen, Ben Kehoe, Animesh Garg, Seth McFarland, Sachin Patil +4

ICRA 2015poster

Automating repetitive surgical subtasks such as suturing, cutting and debridement can reduce surgeon fatigue and procedure times and facilitate supervised tele-surgery. Programming is difficult because human tissue is deformable and highly specular. Using the da Vinci Research Kit (DVRK) robotic sur…

Cited by 239 Source ↗Scholar ↗

2015

Models of human-centered automation in a debridement task

Kirk A. Nichols, Adithyavairavan Murali, Siddarth Sen, Ken Goldberg, Allison M. Okamura

IROS 2015poster

In robot-assisted surgery, manipulation tasks can be achieved through collaboration among robotic and human agents. Collaboration models can potentially include multiple agents working towards a shared objective - a scenario referred to as multilateral manipulation. In this work, we examine multilat…

Cited by 12 Source ↗Scholar ↗