Jayadeep Jacob — accepted AI-conference papers

2026

Deformable Cluster Manipulation Via Whole-Arm Policy Learning

Jayadeep Jacob, Wenzheng Zhang, Houston Warren, Paulo Vinicius Koerich Borges, Fabio Ramos, Tirthankar Bandyopadhyay

ICRA 2026poster

Manipulating clusters of deformable objects presents a substantial challenge with widespread applicability, but requires contact-rich whole-arm interactions. A potential solution must address the limited capacity for realistic model synthesis, high uncertainty in perception, and the lack of efficien…

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2025

Diverse Motion Planning with Stein Diffusion Trajectory Inference

Zeya Yin, Tin Lai, Lucas Barcelos, Jayadeep Jacob, Yonghui Li, Fabio Ramos

ICRA 2025

Acquiring prior knowledge of trajectory distributions in specific environments can significantly expedite the optimisation process in robot motion planning. Leveraging successful past plans and utilising trajectory generative models as priors offers a clear advantage. Previous studies have proposed

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2024

Learning to Simulate Tree-Branch Dynamics for Manipulation

Jayadeep Jacob, Tirthankar Bandyopadhyay, Jason Williams, Paulo V. K. Borges, Fabio Ramos

RA-L 2024

We propose to use a simulation driven inverse inference approach to model the dynamics of tree branches under manipulation. Learning branch dynamics and gaining the ability to manipulate deformable vegetation can help with occlusion-prone tasks, such as fruit picking in dense foliage, as well as mov

Cited by 9 Source ↗Scholar ↗

2024

Stein Movement Primitives for Adaptive Multi-Modal Trajectory Generation

Zeya Yin, Tin Lai, Subhan Khan, Jayadeep Jacob, Yonghui Li, Fabio Ramos

IROS 2024poster

Probabilistic Movement Primitives (ProMPs) and their variants are powerful methods for enabling robots to learn complex tasks from human demonstrations, where motion trajectories are represented as stochastic processes with Gaussian assumptions. However, despite their computational efficiency, these…

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