arXiv subjects

Revanth Krishna Senthilkumaran

Publications and source records attributed to Revanth Krishna Senthilkumaran.

6 recordsLinked to original sources

arXiv2026

REMM: Revisable Embodied Memory for Manipulation

Long-horizon manipulation requires robots to retain task-relevant history while revising conclusions no longer supported by physical evidence. Retaining observations alone does not specify which completed conditions should be withdrawn after a disturbance or which obligations require repair. We introduce REMM, Revisable Embodied Memory for Manipulation, a supervisory memory layer for a frozen G0.5 vision-language-action executor. REMM separates an append-only evidence history from revisable task state and explicitly links derived relations and completed conditions to their supporting facts. When accepted evidence changes a supporting fact, it withdraws dependent conclusions and schedules targeted recovery at verified skill boundaries without updating the executor. Across 51 physical trials, including operator-assisted runs, REMM improves mean recorded task progress over the strongest baseline from 57% to 91% in intervention recovery and from 43% to 70% in sequence recall. Complementary video diagnostics show improved grounding of retained history into the next instruction, including comparisons with $π_{0.5}$-compatible high-level planner adaptations. These results support revisable memory as a practical interface for continued manipulation without policy retraining. Project: https://revanthsenthil.github.io/remm/.

cs.RO↗

arXiv2026

Learning Versatile Humanoid Manipulation with Touch Dreaming

Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stability, end-effector dexterity, and contact-aware interaction under frequent contact changes. In this work, we study dexterous, contact-rich humanoid loco-manipulation. We first develop an RL-based lower-body controller that serves as the stability backbone for whole-body execution during complex manipulation. Building on this controller, we develop a VR-based whole-body humanoid data collection system that integrates dexterous hands and tactile sensing for contact-rich manipulation. We then propose Humanoid Transformer with Touch Dreaming (HTD), a multimodal encoder-decoder Transformer that models touch as a core modality alongside multi-view vision and proprioception. HTD is trained in a single stage with behavioral cloning augmented by touch dreaming: in addition to predicting action chunks, the policy predicts future hand-joint forces and future tactile latents, with tactile-latent targets provided by an exponential moving average target encoder without requiring a separate tactile pretraining stage. This encourages the policy to learn contact-aware representations for dexterous manipulation. Across five real-world contact-rich tasks, HTD achieves a 90.9% relative improvement in average success rate over the stronger baseline for each task. Ablation results further show that latent-space tactile prediction is more effective than raw tactile prediction, yielding a 30% relative gain in success rate. These results demonstrate that our touch-dreaming-enhanced learning system enables versatile, high-dexterity humanoid manipulation in the real world. More information and open-source materials are available at humanoid-touch-dream.github.io.

cs.RO↗

arXiv2024

MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks

This paper presents MOCAS, a multimodal dataset dedicated for human cognitive workload (CWL) assessment. In contrast to existing datasets based on virtual game stimuli, the data in MOCAS was collected from realistic closed-circuit television (CCTV) monitoring tasks, increasing its applicability for real-world scenarios. To build MOCAS, two off-the-shelf wearable sensors and one webcam were utilized to collect physiological signals and behavioral features from 21 human subjects. After each task, participants reported their CWL by completing the NASA-Task Load Index (NASA-TLX) and Instantaneous Self-Assessment (ISA). Personal background (e.g., personality and prior experience) was surveyed using demographic and Big Five Factor personality questionnaires, and two domains of subjective emotion information (i.e., arousal and valence) were obtained from the Self-Assessment Manikin (SAM), which could serve as potential indicators for improving CWL recognition performance. Technical validation was conducted to demonstrate that target CWL levels were elicited during simultaneous CCTV monitoring tasks; its results support the high quality of the collected multimodal signals.

cs.DB↗

arXiv2024

ARTEMIS: AI-driven Robotic Triage Labeling and Emergency Medical Information System

Mass casualty incidents (MCIs) pose a significant challenge to emergency medical services by overwhelming available resources and personnel. Effective victim assessment is the key to minimizing casualties during such a crisis. We introduce ARTEMIS, an AI-driven Robotic Triage Labeling and Emergency Medical Information System, to aid first responders in MCI events. It leverages speech processing, natural language processing, and deep learning to help with acuity classification. This is deployed on a quadruped that performs victim localization and preliminary injury severity assessment. First responders access victim information through a Graphical User Interface that is updated in real-time. To validate our proposed algorithmic triage protocol, we used the Unitree Go1 quadruped. The robot identifies humans, interacts with them, gets vitals and information, and assigns an acuity label. Simulations of an MCI in software and a controlled environment outdoors were conducted. The system achieved a triage-level classification precision of over 74% on average and 99% for the most critical victims, i.e. level 1 acuity, outperforming state-of-the-art deep learning-based triage labeling systems. In this paper, we showcase the potential of human-robot interaction in assisting medical personnel in MCI events.

cs.RO↗

arXiv2023

UPPLIED: UAV Path Planning for Inspection through Demonstration

In this paper, a new demonstration-based path-planning framework for the visual inspection of large structures using UAVs is proposed. We introduce UPPLIED: UAV Path PLanning for InspEction through Demonstration, which utilizes a demonstrated trajectory to generate a new trajectory to inspect other structures of the same kind. The demonstrated trajectory can inspect specific regions of the structure and the new trajectory generated by UPPLIED inspects similar regions in the other structure. The proposed method generates inspection points from the demonstrated trajectory and uses standardization to translate those inspection points to inspect the new structure. Finally, the position of these inspection points is optimized to refine their view. Numerous experiments were conducted with various structures and the proposed framework was able to generate inspection trajectories of various kinds for different structures based on the demonstration. The trajectories generated match with the demonstrated trajectory in geometry and at the same time inspect the regions inspected by the demonstration trajectory with minimum deviation. The experimental video of the work can be found at https://youtu.be/YqPx-cLkv04.

cs.RO↗

arXiv2022

SMARTmBOT: A ROS2-based Low-cost and Open-source Mobile Robot Platform

This paper introduces SMARTmBOT, an open-source mobile robot platform based on Robot Operating System 2 (ROS2). The characteristics of the SMARTmBOT, including low-cost, modular-typed, customizable and expandable design, make it an easily achievable and effective robot platform to support broad robotics research and education involving either single-robot or multi-robot systems. The total cost per robot is approximately $210, and most hardware components can be fabricated by a generic 3D printer, hence allowing users to build the robots or replace any broken parts conveniently. The SMARTmBot is also equipped with a rich range of sensors, making it competent for general task scenarios, such as point-to-point navigation and obstacle avoidance. We validated the mobility and function of SMARTmBOT through various robot navigation experiments and applications with tasks including go-to-goal, pure-pursuit, line following, and swarming. All source code necessary for reading sensors, streaming from an embedded camera, and controlling the robot including robot navigation controllers is available through an online repository that can be found at https://github.com/SMARTlab-Purdue/SMARTmBOT.

cs.RO↗