Author name cluster

Kai Lu 0003

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

IROS Conference 2024 Conference Paper

Learning Generalizable Manipulation Policy with Adapter-Based Parameter Fine-Tuning

Kai Lu 0003
Kim Tien Ly
William Hebberd
Kaichen Zhou
Ioannis Havoutis
Andrew Markham

This study investigates the use of adapters in reinforcement learning for robotic skill generalization across multiple robots and tasks. Traditional methods are typically reliant on robot-specific retraining and face challenges such as efficiency and adaptability, particularly when scaling to robots with varying kinematics. We propose an alternative approach where a disembodied (virtual) hand manipulator learns a task (i. e. , an abstract skill) and then transfers it to various robots with different kinematic constraints without retraining the entire model (i. e. , the concrete, physical implementation of the skill). Whilst adapters are commonly used in other domains with strong supervision available, we show how weaker feedback from robotic control can be used to optimize task execution by preserving the abstract skill dynamics whilst adapting to new robotic domains. We demonstrate the effectiveness of our method with experiments conducted in the SAPIEN ManiSkill environment, showing improvements in generalization and task success rates. All code, data, and additional videos are at this GitHub link: https://kl-research.github.io/genrob.

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ICRA Conference 2024 Conference Paper

Learning to Catch Reactive Objects with a Behavior Predictor

Kai Lu 0003
Jia-Xing Zhong
Bo Yang 0027
Bing Wang 0013
Andrew Markham

Tracking and catching moving objects is an important ability for robots in a dynamic world. Whilst some objects have highly predictable state evolution e. g. , the ballistic trajectory of a tennis ball, reactive targets alter their behavior in response to motion of the manipulator. Reactive applications range from gently capturing living animals such as snakes or fish for biological investigations, to smoothly interacting with and assisting a person. Existing works for dynamic catching usually perform target prediction followed by planning, but seldom account for highly non-linear reactive behaviors. Alternatively, Reinforcement Learning (RL) based methods simply treat the target and its motion as part of the observation of the world-state, but perform poorly due to the weak reward signal. In this work, we blend the approach of an explicit, yet learned, target state predictor with RL. We further show how a tightly coupled predictor which ‘observes’ the state of the robot leads to significantly improved anticipatory action, especially with targets that seek to evade the robot following a simple policy. Experiments show that our method achieves an 86. 4% (open plane area) and a 73. 8% (room) success rate on evasive objects, outperforming monolithic reinforcement learning and other techniques. We also demonstrate the efficacy of our approach across varied targets and trajectories. All code, data, and additional videos are at this GitHub link: https://kl-research.github.io/dyncatch.

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ICRA Conference 2023 Conference Paper

Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation

Kai Lu 0003
Bo Yang 0027
Bing Wang 0013
Andrew Markham

Recent works in robotic manipulation through reinforcement learning (RL) or imitation learning (IL) have shown potential for tackling a range of tasks e. g. , opening a drawer or a cupboard. However, these techniques generalize poorly to unseen objects. We conjecture that this is due to the high-dimensional action space for joint control. In this paper, we take an alternative approach and separate the task of learning ‘what to do’ from ‘how to do it’ i. e. , whole-body control. We pose the RL problem as one of determining the skill dynamics for a disembodied virtual manipulator interacting with articulated objects. The whole-body robotic kinematic control is optimized to execute the high-dimensional joint motion to reach the goals in the workspace. It does so by solving a quadratic programming (QP) model with robotic singularity and kinematic constraints. Our experiments on manipulating complex articulated objects show that the proposed approach is more generalizable to unseen objects with large intra-class variations, outperforming previous approaches. The evaluation results indicate that our approach generates more compliant robotic motion and outperforms the pure RL and IL baselines in task success rates. Additional information and videos are available at https://kl-research.github.io/decoupskill.

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ICRA Conference 2020 Conference Paper

Semi-Empirical Simulation of Learned Force Response Models for Heterogeneous Elastic Objects

Yifan Zhu 0020
Kai Lu 0003
Kris Hauser

This paper presents a semi-empirical method for simulating contact with elastically deformable objects whose force response is learned using entirely data-driven models. A point-based surface representation and an inhomogeneous, nonlinear force response model are learned from a robotic arm acquiring force-displacement curves from a small number of poking interactions. The simulator then estimates displacement and force response when the deformable object is in contact with an arbitrary rigid object. It does so by estimating displacements by solving a Hertzian contact model, and sums the expected forces at individual surface points through querying the learned point stiffness models as a function of their expected displacements. Experiments on a variety of challenging objects show that our approach learns force response with sufficient accuracy to generate plausible contact response for novel rigid objects.

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IROS Conference 2019 Conference Paper

Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment

Yuhong Deng
Xiaofeng Guo
Yixuan Wei
Kai Lu 0003
Bin Fang 0003
Di Guo 0002
Huaping Liu 0001
Fuchun Sun 0001

In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and the gripper for grasping the object accordingly. We utilize the affordance map to provide pixel-wise lifting point candidates for the suction cup. To obtain a good affordance map, the active exploration mechanism is introduced to the system. An effective metric is designed to calculate the reward for the current affordance map, and a deep Q-Network (DQN) is employed to guide the robotic hand to actively explore the environment until the generated affordance map is suitable for grasping. Experimental results have demonstrated that the proposed robotic grasping system is able to greatly increase the success rate of the robotic grasping in cluttered scenes.

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