arXiv subjects

Adam Hung

Publications and source records attributed to Adam Hung.

4 recordsLinked to original sources

arXiv2026

3PoinTr: 3D Point Tracks for Learning Manipulation from Unconstrained Human Videos

Learning manipulation policies from human videos could greatly reduce the need for expensive robot demonstrations, but existing approaches typically require restrictive assumptions such as choreographed human motions, predefined keypoints, manual annotations, or known grasp locations. We propose 3PoinTr, a method for pretraining sample-efficient robot policies from unconstrained human videos by predicting dense 3D point tracks. In the unconstrained human demonstration videos, humans are free to follow whatever trajectories and manipulation strategies they see fit, rather than choreographing their motions to mimic a robot. 3PoinTr uses a lightweight visibility-aware transformer to learn how scene points should move from human videos, and then trains a closed-loop multitask robot policy to flexibly extract action-relevant priors from those predicted point tracks. With only 20 action-labeled robot demonstrations, 3PoinTr achieves a 25.0 percentage point higher average success rate than the strongest behavior cloning and video-pretraining baselines on real-world tasks, and a 29.6 percentage point higher average success rate in simulation. Targeted ablations support the key design choices and confirm the benefit of learning from actionless videos. We further show that 3PoinTr's point track prediction transformer outperforms a strong baseline by preserving supervision over partially occluded points. Project page: https://adamhung60.github.io/3PoinTr/.

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arXiv2025

AVO: Amortized Value Optimization for Contact Mode Switching in Multi-Finger Manipulation

Dexterous manipulation tasks often require switching between different contact modes, such as rolling, sliding, sticking, or non-contact contact modes. When formulating dexterous manipulation tasks as a trajectory optimization problem, a common approach is to decompose these tasks into sub-tasks for each contact mode, which are each solved independently. Optimizing each sub-task independently can limit performance, as optimizing contact points, contact forces, or other variables without information about future sub-tasks can place the system in a state from which it is challenging to make progress on subsequent sub-tasks. Further, optimizing these sub-tasks is very computationally expensive. To address these challenges, we propose Amortized Value Optimization (AVO), which introduces a learned value function that predicts the total future task performance. By incorporating this value function into the cost of the trajectory optimization at each planning step, the value function gradients guide the optimizer toward states that minimize the cost in future sub-tasks. This effectively bridges separately optimized sub-tasks, and accelerates the optimization by reducing the amount of online computation needed. We validate AVO on a screwdriver grasping and turning task in both simulation and real world experiments, and show improved performance even with 50% less computational budget compared to trajectory optimization without the value function.

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arXiv2025

MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning

Path planning for robotic exploration is challenging, requiring reasoning over unknown spaces and anticipating future observations. Efficient exploration requires selecting budget-constrained paths that maximize information gain. Despite advances in autonomous exploration, existing algorithms still fall short of human performance, particularly in structured environments where predictive cues exist but are underutilized. Guided by insights from our user study, we introduce MapExRL, which improves robot exploration efficiency in structured indoor environments by enabling longer-horizon planning through a learned policy and global map predictions. Unlike many learning-based exploration methods that use motion primitives as the action space, our approach leverages frontiers for more efficient model learning and longer horizon reasoning. Our framework generates global map predictions from the observed map, which our policy utilizes, along with the prediction uncertainty, estimated sensor coverage, frontier distance, and remaining distance budget, to assess the strategic long-term value of frontiers. By leveraging multiple frontier scoring methods and additional context, our policy makes more informed decisions at each stage of the exploration. We evaluate our framework on a real-world indoor map dataset, achieving up to an 18.8% improvement over the strongest state-of-the-art baseline, with even greater gains compared to conventional frontier-based algorithms. Website: https://mapexrl.github.io

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arXiv2024

RoPotter: Toward Robotic Pottery and Deformable Object Manipulation with Structural Priors

Humans are capable of continuously manipulating a wide variety of deformable objects into complex shapes. This is made possible by our intuitive understanding of material properties and mechanics of the object, for reasoning about object states even when visual perception is occluded. These capabilities allow us to perform diverse tasks ranging from cooking with dough to expressing ourselves with pottery-making. However, developing robotic systems to robustly perform similar tasks remains challenging, as current methods struggle to effectively model volumetric deformable objects and reason about the complex behavior they typically exhibit. To study the robotic systems and algorithms capable of deforming volumetric objects, we introduce a novel robotics task of continuously deforming clay on a pottery wheel. We propose a pipeline for perception and pottery skill-learning, called RoPotter, wherein we demonstrate that structural priors specific to the task of pottery-making can be exploited to simplify the pottery skill-learning process. Namely, we can project the cross-section of the clay to a plane to represent the state of the clay, reducing dimensionality. We also demonstrate a mesh-based method of occluded clay state recovery, toward robotic agents capable of continuously deforming clay. Our experiments show that by using the reduced representation with structural priors based on the deformation behaviors of the clay, RoPotter can perform the long-horizon pottery task with 44.4% lower final shape error compared to the state-of-the-art baselines.

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