2 citations · 4 across the 17 of their papers we have counts for
19 papers
Beyond Policy Alignment: Closing the Planning-Learning Loop for Robot Control with Learned World Models
Kowndinya Boyalakuntla, Yuhan Liu, Abdeslam Boularias
Planning with learned world models combines online trajectory optimization with learned value and policy functions for high-dimensional control. Because the planner determines the…
Plan-Conditioned Imitation for Robust Object Retrieval under Self-Occlusion in Dense Clutter
Kowndinya Boyalakuntla, Ajinkya Pawar, Abdeslam Boularias +1
Retrieving objects from dense clutter requires rearrangement during which the manipulator can occlude objects while moving them. Repeated arm withdrawals to restore visibility inte…
Foresight Residual RL for Long-Horizon Robot Manipulation with Vision-Language-Action Models
Yuhan Liu, Xinyu Zhang, Litao Liu +1
Vision-Language-Action (VLA) policies offer strong general-purpose manipulation priors, but often fail on tight-tolerance, contact-rich assembly due to long-horizon credit assignme…
Learning Visual Feature-Based World Models via Residual Latent Action
Xinyu Zhang, Zhengtong Xu, Yutian Tao +3
World models predict future transitions from observations and actions. Existing works predominantly focus on image generation only. Visual feature-based world models, on the other…
Glove2Hand: Synthesizing Natural Hand-Object Interaction from Multi-Modal Sensing Gloves
Xinyu Zhang, Ziyi Kou, Chuan Qin +7
Understanding hand-object interaction (HOI) is fundamental to computer vision, robotics, and AR/VR. However, conventional hand videos often lack essential physical information such…
Bounding Distributional Shifts in World Modeling through Novelty Detection
Eric Jing, Abdeslam Boularias
Recent work on visual world models shows significant promise in latent state dynamics obtained from pre-trained image backbones. However, most of the current approaches are sensiti…
Failure Forecasting Boosts Robustness of Sim2Real Rhythmic Insertion Policies
Yuhan Liu, Xinyu Zhang, Haonan Chang +1
This paper addresses the challenges of Rhythmic Insertion Tasks (RIT), where a robot must repeatedly perform high-precision insertions, such as screwing a nut into a bolt with a wr…
KARL: Kalman-Filter Assisted Reinforcement Learner for Dynamic Object Tracking and Grasping
Kowndinya Boyalakuntla, Abdeslam Boularias, Jingjin Yu
We present Kalman-filter Assisted Reinforcement Learner (KARL) for dynamic object tracking and grasping over eye-on-hand (EoH) systems, significantly expanding such systems capabil…
Integrating Model-based Control and RL for Sim2Real Transfer of Tight Insertion Policies
Isidoros Marougkas, Dhruv Metha Ramesh, Joe H. Doerr +4
Object insertion under tight tolerances () is an important but challenging assembly task as even small errors can result in undesirable contacts. Recent effo…
PROBE: Proprioceptive Obstacle Detection and Estimation while Navigating in Clutter
Dhruv Metha Ramesh, Aravind Sivaramakrishnan, Shreesh Keskar +3
In critical applications, including search-and-rescue in degraded environments, blockages can be prevalent and prevent the effective deployment of certain sensing modalities, parti…
Motion Blender Gaussian Splatting for Dynamic Scene Reconstruction
Xinyu Zhang, Haonan Chang, Yuhan Liu +1
Gaussian splatting has emerged as a powerful tool for high-fidelity reconstruction of dynamic scenes. However, existing methods primarily rely on implicit motion representations, s…
Autoregressive Action Sequence Learning for Robotic Manipulation
Xinyu Zhang, Yuhan Liu, Haonan Chang +2
Designing a universal policy architecture that performs well across diverse robots and task configurations remains a key challenge. In this work, we address this by representing ro…
DAP: Diffusion-based Affordance Prediction for Multi-modality Storage
Haonan Chang, Kowndinya Boyalakuntla, Yuhan Liu +3
Solving storage problem: where objects must be accurately placed into containers with precise orientations and positions, presents a distinct challenge that extends beyond traditio…
Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization
Liam Schramm, Abdeslam Boularias
Monte Carlo tree search (MCTS) has been successful in a variety of domains, but faces challenges with long-horizon exploration when compared to sampling-based motion planning algor…
Bellman Diffusion Models
Liam Schramm, Abdeslam Boularias
Diffusion models have seen tremendous success as generative architectures. Recently, they have been shown to be effective at modelling policies for offline reinforcement learning a…
A3VLM: Actionable Articulation-Aware Vision Language Model
Siyuan Huang, Haonan Chang, Yuhan Liu +5
Vision Language Models (VLMs) have received significant attention in recent years in the robotics community. VLMs are shown to be able to perform complex visual reasoning and scene…
One-Shot Imitation Learning with Invariance Matching for Robotic Manipulation
Xinyu Zhang, Abdeslam Boularias
Learning a single universal policy that can perform a diverse set of manipulation tasks is a promising new direction in robotics. However, existing techniques are limited to learni…
Scaling Manipulation Learning with Visual Kinematic Chain Prediction
Xinyu Zhang, Yuhan Liu, Haonan Chang +1
Learning general-purpose models from diverse datasets has achieved great success in machine learning. In robotics, however, existing methods in multi-task learning are typically co…
OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data
Shiyang Lu, Haonan Chang, Eric Pu Jing +2
This work presents OVIR-3D, a straightforward yet effective method for open-vocabulary 3D object instance retrieval without using any 3D data for training. Given a language query,…