Author name cluster

Keng Peng Tee

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.

16 papers

1 author row

ICRA Conference 2025 Conference Paper

RMP-YOLO: A Robust Motion Predictor for Partially Observable Scenarios Even if You Only Look Once

Jiawei Sun 0006
Jiahui Li
Tingchen Liu
Chengran Yuan
Shuo Sun
Zefan Huang
Anthony Wong
Keng Peng Tee

We introduce RMP-YOLO, a unified framework designed to provide robust motion predictions even with incomplete input data. Our key insight stems from the observation that complete and reliable historical trajectory data plays a pivotal role in ensuring accurate motion prediction. Therefore, we propose a new paradigm that prioritizes the reconstruction of intact historical trajectories before feeding them into the prediction modules. Our approach introduces a novel scene tokenization module to enhance the extraction and fusion of spatial and temporal features. Following this, our proposed recovery module reconstructs agents' incomplete historical trajectories by leveraging local map topology and interactions with nearby agents. The reconstructed, clean historical data is then integrated into the downstream prediction modules. Our framework is able to effectively handle missing data of varying lengths and remains robust against observation noise while maintaining high prediction accuracy. Furthermore, our recovery module is compatible with existing prediction models, ensuring seamless integration. Extensive experiments validate the effectiveness of our approach, and deployment in real-world autonomous vehicles confirms its practical utility. In the 2024 Waymo Motion Prediction Competition, our method, RMP-YOLO, achieves state-of-the-art performance, securing third place. Our code is open-source at https://github.com/ggosjw/RMP-YOLO.

ICRA Conference 2021 Conference Paper

Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning

Cihan Acar
Keng Peng Tee

Sampling-based motion planning under task constraints is challenging because the null-measure constraint manifold in the configuration space makes rejection sampling extremely inefficient, if not impossible. This paper presents a learning-based sampling strategy for constrained motion planning problems. We investigate the use of two well-known deep generative models, the Conditional Variational Autoencoder (CVAE) and the Conditional Generative Adversarial Net (CGAN), to generate constraint-satisfying sample configurations. Instead of precomputed graphs, we use generative models conditioned on constraint parameters for approximating the constraint manifold. This approach allows for the efficient drawing of constraint-satisfying samples online without any need for modification of available sampling-based motion planning algorithms. We evaluate the efficiency of these two generative models in terms of their sampling accuracy and coverage of sampling distribution. Simulations and experiments are also conducted for different constraint tasks on two robotic platforms.

IROS Conference 2021 Conference Paper

GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping

Anil Kurkcu
Cihan Acar
Domenico Campolo
Keng Peng Tee

The domain of robotics is challenging to apply deep reinforcement learning due to the need for large amounts of data and for ensuring safety during learning. Curriculum learning has shown good performance in terms of sample-efficient deep learning. In this paper, we propose an algorithm (named GloCAL) that creates a curriculum for an agent to learn multiple discrete tasks, based on clustering tasks according to their evaluation scores. From the highest-performing cluster, a global task representative of the cluster is identified for learning a global policy that transfers to subsequently formed new clusters, while remaining tasks in the cluster are learnt as local policies. The efficacy and efficiency of our GloCAL algorithm are compared with other approaches in the domain of grasp learning for 49 objects with varied object complexity and grasp difficulty from the EGAD! dataset. The results show that GloCAL is able to learn to grasp 100% of the objects, whereas other approaches achieve at most 86% despite being given 1. 5× longer training time.

ICRA Conference 2021 Conference Paper

State Estimation for Hybrid Wheeled-Legged Robots Performing Mobile Manipulation Tasks

Yangwei You
Samuel Cheong
Tai Pang Chen
Yuda Chen
Kun Zhang
Cihan Acar
Fon Lin Lai
Albertus H. Adiwahono

This paper introduces a general state estimation framework fusing multiple sensor information for hybrid wheeled-legged robots performing mobile manipulation tasks. At the core of the state estimator is a novel unified odometry for hybrid locomotion which can seamlessly maintain tracking and has no need to switch between stepping and rolling modes. To the best of our knowledge, the proposed odometry is the first work in this area. It is calculated based on the robot kinematics and instantaneous contact points of wheels with sensor inputs from IMU, joint encoders, joint torque sensors estimating wheel contact status, as well as RGB-D camera detecting geometric features of the terrain (e. g. elevation and surface normal vector). Subsequently, the odometry output is utilized as the motion model of a 3D Lidar map-based Monte Carlo Localization module for drift-free state estimation. As part of the framework, visual localization is integrated to provide high precision guidance for the robot movement relative to an object of interest. The proposed approach was verified thoroughly by two experiments conducted on the Pholus robot with OptiTrack measurements as ground truth.

IROS Conference 2021 Conference Paper

Supervised Autonomy for Remote Teleoperation of Hybrid Wheel-Legged Mobile Manipulator Robots

Samuel Cheong
Tai Pang Chen
Cihan Acar
Yangwei You
Yuda Chen
Wan Leong Sim
Keng Peng Tee

This paper proposes an improved supervised autonomy framework for remote teleoperation of a quadrupedal bimanual mobile manipulator in an unknown environment, with the usage of advanced perception technology and allowing the operator to easily assist the robot with decision making for executing tasks on the fly. First, the perception system uses lightweight deep neural network-based Single Shot Detector (SSD) MobileNet on RGB images to detect objects and highlight them to the human operator via an intuitive interactive visualization interface. After object and action selections are made by the operator, segmentation of object point cloud and 3D surfaces based on random sample consensus is performed, followed by object pose localization by using keypoint extraction. Based on the localized object, mobile manipulation motion to perform the operator-selected action is planned and executed with the help of a state estimator for the hybrid wheel-legged robot. Thanks to the autonomy of the robot in perception and manipulation, the complexity of teleoperating the robot is reduced to specifying the essential task objectives. Experimental results on the real robot, with full system integration, for 2 task scenarios, namely passage clearing and object retrieval, demonstrate a high average success rate of 92. 2% over a total of 90 trials.

IROS Conference 2020 Conference Paper

KOVIS: Keypoint-based Visual Servoing with Zero-Shot Sim-to-Real Transfer for Robotics Manipulation

En Yen Puang
Keng Peng Tee
Wei Jing

We present KOVIS, a novel learning-based, calibration-free visual servoing method for fine robotic manipulation tasks with eye-in-hand stereo camera system. We train the deep neural network only in the simulated environment; and the trained model could be directly used for real-world visual servoing tasks. KOVIS consists of two networks. The first keypoint network learns the keypoint representation from the image using with an autoencoder. Then the visual servoing network learns the motion based on keypoints extracted from the camera image. The two networks are trained end-to-end in the simulated environment by self-supervised learning without manual data labeling. After training with data augmentation, domain randomization, and adversarial examples, we are able to achieve zero-shot sim-to-real transfer to real-world robotic manipulation tasks. We demonstrate the effectiveness of the proposed method in both simulated environment and real-world experiment with different robotic manipulation tasks, including grasping, peg-in-hole insertion with 4mm clearance, and M13 screw insertion. The demo video is available at: http://youtube/gfBJBR2tDzA.

IROS Conference 2019 Conference Paper

Unified Human-Robot Shared Control with Application to Haptic Telemanipulation

Samuel Cheong
Keng Peng Tee

Human-robot shared control (SC) has largely been studied in two complementary forms, namely divisible shared control (DSC) and interactive shared control (ISC). DSC enables clean division of the human and the robot subtasks, thus enabling them to work independently, while ISC allows for flexible intervention to improve the collaborative performance or experience. This paper presents a unified scheme that combines both forms of SCs to attain the benefits of flexibility as well as ease of use when human and robot jointly work on a task together. Based on the idea that flexibility should be embedded in every task constraint that the robot is controlling, we connect ISC into the robot subtask providing a soft boundary between the divided orthogonal subspaces allowing human to access the robot subtask and intervene whenever necessary. We also propose a new simple yet effective Cartesian stiffness adaptation law that enables the robot to modify its endpoint stiffness in the robot’s control subspace in the presence of disagreement from the human. Simulations and real robot studies for a teleoperated path-following scenario were performed to demonstrate the flexibility of the unified shared control (USC), which allows the robot to dynamically adapt its task based on the operator’s intentions.

IROS Conference 2018 Conference Paper

Multi-Modal Robot Apprenticeship: Imitation Learning Using Linearly Decayed DMP+ in a Human-Robot Dialogue System

Yan Wu 0002
Ruohan Wang
Luis F. D'Haro
Rafael E. Banchs
Keng Peng Tee

Robot learning by demonstration gives robots the ability to learn tasks which they have not been programmed to do before. The paradigm allows robots to work in a greater range of real-world applications in our daily life. However, this paradigm has traditionally been applied to learn tasks from a single demonstration modality. This restricts the approach to be scaled to learn and execute a series of tasks in a real-life environment. In this paper, we propose a multi-modal learning approach using DMP+ with linear decay integrated in a dialogue system with speech and ontology for the robot to learn seamlessly through natural interaction modalities (like an apprentice) while learning or re-learning is done on the fly to allow partial updates to a learned task to reduce potential user fatigue and operational downtime in teaching. The performance of new DMP+ with linear decay system is statistically benchmarked against state-of-the-art DMP implementations. A gluing demonstration is also conducted to show how the system provides seamless learning of multiple tasks in a flexible manufacturing set-up.

ICRA Conference 2018 Conference Paper

Towards Emergence of Tool Use in Robots: Automatic Tool Recognition and Use Without Prior Tool Learning

Keng Peng Tee
Jun Li 0005
Tai Pang Chen
Kong-Wah Wan
Gowrishankar Ganesh

Humans are adept at tool use. We can intuitively and immediately improvise and use unknown objects in our environment as tools, to assist us in performing tasks. In this study, we provide similar cognition and capabilities to robots. Neuroscientific studies on tool use have suggested that human dexterity with tools is enabled by the embodiment of the tools, which in effect, allows humans to immediately transfer prior skills acquired without tools, onto tasks requiring tool use. Here, utilizing the theoretical results from our investigations on embodiment and tool use in humans over the last years, we propose a concept and algorithm to enable similar skill transfer by robots. Our algorithm enables a robot that has had no prior learning with tools, to automatically recognize an object (seen for the first time) in its environment as a potential tool for an otherwise unattainable task, and use the tool to perform the task thereafter.

IROS Conference 2016 Conference Paper

Dynamic Movement Primitives Plus: For enhanced reproduction quality and efficient trajectory modification using truncated kernels and Local Biases

Ruohan Wang
Yan Wu 0002
Wei Liang Chan
Keng Peng Tee

Dynamic Movement Primitives (DMPs) are a generic approach for trajectory modeling in an attractor land-scape based on differential dynamical systems. DMPs guarantee stability and convergence properties of learned trajectories, and scale well to high dimensional data. In this paper, we propose DMP+, a modified formulation of DMPs which, while preserving the desirable properties of the original, 1) achieves lower mean square error (MSE) with equal number of kernels, and 2) allows learned trajectories to be efficiently modified by updating a subset of kernels. The ability to efficiently modify learned trajectories i) improves reusability of existing primitives, and ii) reduces user fatigue during imitation learning as errors during demonstration may be corrected later without requiring another complete demonstration. In addition, DMP+ may be used with existing DMP techniques for trajectory generalization and thus complements them. We compare the performance of our proposed approach against DMPs in learning trajectories of handwritten characters, and show that DMP+ achieves lower MSE in position deviation. We demonstrate in a second experiment that DMP+ can efficiently update a learned trajectory by updating only a subset of kernels. The update algorithm achieves modeling accuracy comparable to learning the adapted trajectory with the original DMPs.

IROS Conference 2015 Conference Paper

Adaptive optimal control for coordination in physical human-robot interaction

Yanan Li 0001
Keng Peng Tee
Rui Yan 0005
Wei Liang Chan
Yan Wu 0002
Dilip Kumar Limbu

In this paper, we propose an adaptive optimal control for a robot to collaborate with a human. Game theory and policy iteration are employed to analyze the interactive behaviors of the human and the robot in physical interactions. The human's control objective is estimated and it is used to adapt the robot's own objective, such that human-robot coordination can be achieved. An optimal control is developed to guarantee that the robot's control objective is realized. The validity of the proposed method is verified through rigorous analysis and experiment studies.

ICRA Conference 2015 Conference Paper

Role adaptation of human and robot in collaborative tasks

Yanan Li 0001
Keng Peng Tee
Wei Liang Chan
Rui Yan 0005
Yuanwei Chua
Dilip Kumar Limbu

In this paper, a role adaptation method is developed for human-robot collaboration based on game theory. This role adaptation is engaged whenever the interaction force changes, causing the proportion of control sharing between human and robot to vary. In one boundary condition, the robot takes full control of the system when there is no human intervention. In the other boundary condition, it becomes a follower when the human exhibits strong intention to lead the task. Experimental results show that the proposed method yields better overall performance than fixed-role interactions.

IROS Conference 2014 Conference Paper

Gesture-based attention direction for a telepresence robot: Design and experimental study

Keng Peng Tee
Rui Yan 0005
Yuanwei Chua
Zhiyong Huang 0001
Somchaya Liemhetcharat

The application of robotics to telepresence can enhance user interaction experience by providing embodiment, engaging behaviors, automatic control, and human perception. This paper presents a new telepresence robot with gesture-based attention direction to orient the robot towards attention targets according to human deictic gestures. Gesture-based attention direction is realized by combining Localist Attractor Network (LAN) and Short-Term Memory (STM). We also propose audio-visual fusion based on context-dependent prioritization among the 3 types of audio-visual cues (gesture, speech source location, head location). Experiment results are very promising and show that i) the average gesture recognition rate is 92%, i) gesture-based attention direction rate is 90%, and that ii) only by considering the 3 types of audio-visual cues together can the robot perform on par with a human in directing attention to the correct person in a meeting scenario.

IROS Conference 2012 Conference Paper

Adaptive control for robot manipulators under ellipsoidal task space constraints

Keng Peng Tee
Shuzhi Sam Ge
Rui Yan 0005
Haizhou Li 0001

Motivated by applications in robot-assisted physical rehabilitation, this paper presents an adaptive control design for robot manipulators operating in an ellipsoidal constrained region. The ellipsoidal constraint problem is more challenging than the box constraint problem tackled in previous works, since the nonlinear constraint boundary cannot be handled in a decoupled manner along the dimensions of the task space. We introduce a novel Barrier Lyapunov Function (BLF) which contains a quotient of the squared norm of the tracking error over the ellipsoidal task space constraint. This function allows the task space constraint to be handled directly without requiring an intermediate mapping to the error space. We show that, under the proposed BLF-based adaptive control, the end-effector always remains in the constrained region despite the perturbing effects of online parameter adaptation and also the presence of bounded external disturbances. A simulation example illustrates the performance of the proposed control.

ICRA Conference 2011 Conference Paper

Model-free impedance control for safe human-robot interaction

Yanan Li 0001
Shuzhi Sam Ge
Chenguang Yang 0001
Xinyang Li
Keng Peng Tee

In this paper, model-free impedance control is designed for the safe human-robot interaction. A passive impedance model is imposed on the robot and a control method is proposed to guarantee the robot dynamics governed by the target model. The proposed method does not require any model information except for upper bounds of system matrix. It is thus easy to apply to practical implementation. The rigorous analysis of the control performance and robustness is presented. The validity of the proposed method is verified on the six degrees-of-freedom (DOF) PUMA 560 robot arm through simulation.

ICRA Conference 2010 Conference Paper

Adaptive admittance control of a robot manipulator under task space constraint

Keng Peng Tee
Rui Yan 0005
Haizhou Li 0001

We present adaptive admittance control of a robotic manipulator, with uncertain dynamic parameters, operating in a constrained task space. To provide compliance to external forces, we generate a differentiable reference trajectory that remains in the constrained task space. Then, adaptive backstepping control, based on a time-varying asymmetric Barrier Lyapunov Function (BLF), is designed to achieve tracking of the reference trajectory while guaranteeing constraint satisfaction. The improved BLF-based control renders the entire constrained task space positively invariant. Despite transient perturbations by external forces and online parameter adaptation, practical tracking of the reference trajectory is achieved without transgression of the constrained task space. In the absence of interaction forces, asymptotic tracking of the desired trajectory is achieved.