arXiv subjects

Kenzhi Iskandar Wong

Publications and source records attributed to Kenzhi Iskandar Wong.

3 recordsLinked to original sources

arXiv2026

Dual-Arm Box Manipulation via Online Inertial Estimation and Convex Wrench Optimization

Dual-arm robotic manipulation often requires lifting and transporting objects whose mass and center of mass are not known in advance. These uncertainties affect the force-moment balance required for stable lifting, and improper regulation of contact wrenches can lead to slip, object drop, orientation deviation, or excessive squeezing. This paper presents a friction-aware dual-arm box-manipulation framework for objects with unknown inertial properties. The framework combines online inertial estimation with convex contact-wrench optimization. The inertial estimation module estimates the object mass and center of mass from measured contact wrenches, while the optimization module computes friction-feasible contact forces and torsional moments through a second-order cone program (SOCP) under ellipsoidal friction-limit-surface constraints. An offline trajectory refinement stage is also included to reduce undesired object-environment contact when geometric constraints are present. By enforcing friction feasibility as a hard constraint and minimizing contact effort within the feasible region, the framework achieves stable lifting without treating slip avoidance and excessive squeezing as separately tuned objectives. Experiments on a real dual-arm robotic system under different center-of-mass configurations demonstrate that the method lifts objects with unknown inertial properties while maintaining stable frictional contact.

cs.RO↗

arXiv2026

Demonstration-Based Robotic Feeding Using Dynamic Movement Primitives

This paper presents a demonstration-based robotic feeding framework that integrates human demonstration, Dynamic Movement Primitive-based motion planning, vision-based scene perception, and robot control. A human operator first demonstrates the scooping and feeding motions while the position and Euler-angle orientation of the spoon are recorded using a motion-capture system. The recorded trajectory is segmented into scooping and feeding motions, which are encoded using separate Dynamic Movement Primitives. Before task execution, an RGB-D camera estimates the participant's mouth position, while two AprilTags are used for robot-frame registration and tray localization. The detected tray pose is used to position the scooping motion, whereas the detected mouth position defines the positional goal of the feeding motion. The approach, scooping, and feeding trajectories are then executed sequentially by a Kinova Gen3 manipulator through a ROS~2-based framework. A real-world experiment demonstrates the feasibility of the integrated pipeline and shows that the learned spoon motions can be reproduced within the detected experimental scene.

cs.RO↗

arXiv2025

Generalizing Robot Trajectories from Single-Context Human Demonstrations: A Probabilistic Approach

Generalizing robot trajectories from human demonstrations to new contexts remains a key challenge in Learning from Demonstration (LfD), particularly when only single-context demonstrations are available. We present a novel Gaussian Mixture Model (GMM)-based approach that enables systematic generalization from single-context demonstrations to a wide range of unseen start and goal configurations. Our method performs component-level reparameterization of the GMM, adapting both mean vectors and covariance matrices, followed by Gaussian Mixture Regression (GMR) to generate smooth trajectories. We evaluate the approach on a dual-arm pick-and-place task with varying box placements, comparing against several baselines. Results show that our method significantly outperforms baselines in trajectory success and fidelity, maintaining accuracy even under combined translational and rotational variations of task configurations. These results demonstrate that our method generalizes effectively while ensuring boundary convergence and preserving the intrinsic structure of demonstrated motions.

cs.RO↗