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Jun Yamada

9 accepted 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: 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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2025

COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Grasping

Jun Yamada, Alexander Luis Mitchell, Jack Collins, Ingmar Posner
CoRL 2025poster

This paper addresses the challenge of occluded robot grasping, i.e. grasping in situations where the desired grasp poses are kinematically infeasible due to environmental constraints such as surface collisions. Existing RL methods struggle with task complexity, and collecting expert demonstrations i…

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2025

D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation

Jun Yamada, Shaohong Zhong, Jack Collins, Ingmar Posner
CoRL 2025poster

Mastering deformable object manipulation often necessitates the use of anthropomorphic, high-degree-of-freedom robot hands capable of precise, contact-rich control. However, current trajectory optimisation methods often struggle in these settings due to the large search space and the sparse task inf…

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2024

RAMP: A Benchmark for Evaluating Robotic Assembly Manipulation and Planning

Jack Collins, Mark Robson, Jun Yamada, Mohan Sridharan, Karol Janik, Ingmar Posner
RA-L 2024

We introduce RAMP, an open-source robotics benchmark inspired by real-world industrial assembly tasks. RAMP consists of beams that a robot must assemble into specified goal configurations using pegs as fasteners. As such, it assesses planning and execution capabilities, and poses challenges in perce

Cited by 24Source ↗Scholar ↗
2024

TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer

Jun Yamada, Marc Rigter, Jack Collins, Ingmar Posner
ICRA 2024poster

Model-based RL is a promising approach for real-world robotics due to its improved sample efficiency and generalization capabilities compared to model-free RL. However, effective model-based RL solutions for vision-based real-world applications require bridging the sim-to-real gap for any world mode…

Cited by 6Source ↗Scholar ↗
2023

Leveraging Scene Embeddings for Gradient-Based Motion Planning in Latent Space

Jun Yamada, Chia-Man Hung, Jack Collins, Ioannis Havoutis, Ingmar Posner
ICRA 2023poster

Motion planning framed as optimisation in structured latent spaces has recently emerged as competitive with traditional methods in terms of planning success while significantly outperforming them in terms of computational speed. However, the real-world applicability of recent work in this domain rem…

Cited by 10Source ↗Scholar ↗
2020

Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments

Jun Yamada, Youngwoon Lee, Gautam Salhotra, Karl Pertsch, Max Pflueger, Gaurav Sukhatme +2
CoRL 2020

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent

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