arXiv subjects

Jeremy Siburian

Publications and source records attributed to Jeremy Siburian.

3 recordsLinked to original sources

arXiv2026

CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding

Vision-language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on robot data and architectural modifications can reshape the backbone so extensively that it becomes difficult to isolate what the VLM contributes to control. Directly converting pretrained VLMs into VLAs with minimal architectural change offers a more transparent path to understanding how VLM capabilities transfer across model scales. The core obstacle is output-distribution mismatch: predicting actions as bare numeric token sequences moves generation away from the VLM's pretrained language distribution, degrading the capabilities we seek to preserve. To address this, we propose CLAP (Causal Language-Action Prediction), which prepends each numeric action sequence with a natural-language action description, causally conditioning precise action-token prediction on a language-action plan without modifying the backbone architecture. With single-epoch fine-tuning alone, 2B CLAP achieves 90.8% on LIBERO (+14.9 pt over VLA-0) and improves robustness on LIBERO-PRO under language, object, and spatial perturbations. We will release CLAP at 0.8B, 2B, and 4B as an open-weight, multi-scale compact VLA family from a single VLM lineage, enabling controlled analysis of VLM-to-VLA capability transfer.

cs.RO↗

arXiv2026

Tactile Memory with Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist

Tactile memory, the ability to store and retrieve touch-based experience, is critical for contact-rich tasks such as key insertion under uncertainty. To replicate this capability, we introduce Tactile Memory with Soft Robot (TaMeSo-bot), a system that integrates a soft wrist with tactile retrieval-based control to enable safe and robust manipulation. The soft wrist allows safe contact exploration during data collection, while tactile memory reuses past demonstrations via retrieval for flexible adaptation to unseen scenarios. The core of this system is the Masked Tactile Trajectory Transformer (MAT$^\text{3}$), which jointly models spatiotemporal interactions between robot actions, distributed tactile cues, force-torque measurements, and proprioceptive signals. Through masked token prediction, MAT$^\text{3}$ learns rich spatiotemporal representations by inferring missing sensory information from context, autonomously extracting task-relevant features without explicit subtask segmentation. We validate our approach on peg-in-hole tasks with diverse pegs and conditions in real-robot experiments. Our extensive evaluation demonstrates that MAT$^\text{3}$ achieves higher success rates than the baselines over all conditions and shows remarkable capability to adapt to unseen pegs and conditions.

cs.RO↗

arXiv2025

Grounded Vision-Language Interpreter for Long-Horizon Bimanual Task and Motion Planning

While recent advances in vision-language models have accelerated language-guided robot planning, their black-box nature lacks the safety guarantees and interpretability crucial for real-world deployment. Conversely, classical symbolic planners offer rigorous safety verification but require significant expert knowledge for setup. Moreover, most existing methods are limited to single-arm pick-and-place tasks, leaving bimanual manipulation largely underexplored, despite its tightly interdependent subtasks and the need to explicitly manage inter-arm collisions. To bridge this gap, this paper proposes ViLaIn-TAMP, a hybrid planning framework for enabling verifiable, interpretable, and autonomous bimanual robot behaviors. ViLaIn-TAMP comprises three main components: (1) a Vision-Language Interpreter (ViLaIn) adapted from a prior work that converts multimodal inputs into structured PDDL problem specifications, (2) an integrated Task and Motion Planning (TAMP) system that grounds these specifications in actionable trajectory sequences through symbolic and geometric constraint reasoning, explicitly verifying feasibility before execution, and (3) a corrective planning (CP) module which receives structured motion failure feedback and feeds it as constraints back to ViLaIn to refine the specification. We design challenging bimanual manipulation tasks in a cooking domain to evaluate our framework, where experimental results show that ViLaIn-TAMP outperforms a VLM-as-a-planner baseline by 17.5% in mean success rate, with the CP module boosting it further by 32.9%. We further validate ViLaIn-TAMP on a physical dual-arm robotic system. Project page: https://omron-sinicx.github.io/ViLaIn-TAMP

cs.RO↗