arXiv subjects

Joaquin Palacios

Publications and source records attributed to Joaquin Palacios.

6 recordsLinked to original sources

arXiv2026

DITTO: Dexterous Interface for Transparent TeleOperation

Collecting data for manipulation with high-DOF hands is challenging, as interfaces must capture rich hand motion while rendering the contact interactions essential for precise manipulation. Existing data collection approaches face a trade-off: teleoperation ensures deployment consistency but lacks force feedback, while handheld (in-the-wild) systems provide natural force transparency but introduce a visual embodiment gap at deployment. We present DITTO, a Dexterous Interface for Transparent TeleOperation, which resolves this through the anatomically informed co-design of a dexterous 7-DOF robotic hand and a kinematically equivalent motorized exoskeleton. A 1-to-1 actuator mapping between the exoskeleton and robotic hand enables handheld (in-the-wild) data collection and bilateral teleoperation with joint-level force feedback unified in a single platform. We demonstrate that the DITTO exoskeleton spans the operator's natural index-to-thumb workspace, and showcase DITTO's dexterous capabilities via learned policies on contact-rich tasks.

cs.RO↗

arXiv2026

SpikeATac: A Multimodal Tactile Finger with Taxelized Dynamic Sensing for Dexterous Manipulation

In this work, we introduce SpikeATac, a multimodal tactile finger combining a taxelized and highly sensitive dynamic response (PVDF) with a static transduction method (capacitive) for multimodal touch sensing. Named for its `spiky' response, SpikeATac's 16-taxel PVDF film sampled at 4 kHz provides fast, sensitive dynamic signals to the very onset and breaking of contact. We characterize the sensitivity of the different modalities, and show that SpikeATac provides the ability to stop quickly and delicately when grasping fragile, deformable objects. Beyond parallel grasping, we show that SpikeATac can be used in a learning-based framework to achieve new capabilities on a dexterous multifingered robot hand. We use reinforcement learning from human feedback to fine-tune the behavior of a policy to modulate force. Our hardware platform and learning pipeline together enable a difficult dexterous and contact-rich task that has not previously been achieved: in-hand manipulation of fragile objects. Videos are available at https://roamlab.github.io/spikeatac/ .

cs.RO↗

arXiv2025

Train Robots in a JIF: Joint Inverse and Forward Dynamics with Human and Robot Demonstrations

Pre-training on large datasets of robot demonstrations is a powerful technique for learning diverse manipulation skills but is often limited by the high cost and complexity of collecting robot-centric data, especially for tasks requiring tactile feedback. This work addresses these challenges by introducing a novel method for pre-training with multi-modal human demonstrations. Our approach jointly learns inverse and forward dynamics to extract latent state representations, towards learning manipulation specific representations. This enables efficient fine-tuning with only a small number of robot demonstrations, significantly improving data efficiency. Furthermore, our method allows for the use of multi-modal data, such as combination of vision and touch for manipulation. By leveraging latent dynamics modeling and tactile sensing, this approach paves the way for scalable robot manipulation learning based on human demonstrations.

cs.RO↗

arXiv2024

Reciprocal Learning of Intent Inferral with Augmented Visual Feedback for Stroke

Intent inferral, the process by which a robotic device predicts a user's intent from biosignals, offers an effective and intuitive way to control wearable robots. Classical intent inferral methods treat biosignal inputs as unidirectional ground truths for training machine learning models, where the internal state of the model is not directly observable by the user. In this work, we propose reciprocal learning, a bidirectional paradigm that facilitates human adaptation to an intent inferral classifier. Our paradigm consists of iterative, interwoven stages that alternate between updating machine learning models and guiding human adaptation with the use of augmented visual feedback. We demonstrate this paradigm in the context of controlling a robotic hand orthosis for stroke, where the device predicts open, close, and relax intents from electromyographic (EMG) signals and provides appropriate assistance. We use LED progress-bar displays to communicate to the user the predicted probabilities for open and close intents by the classifier. Our experiments with stroke subjects show reciprocal learning improving performance in a subset of subjects (two out of five) without negatively impacting performance on the others. We hypothesize that, during reciprocal learning, subjects can learn to reproduce more distinguishable muscle activation patterns and generate more separable biosignals.

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arXiv2024

Grasp Force Assistance via Throttle-based Wrist Angle Control on a Robotic Hand Orthosis for C6-C7 Spinal Cord Injury

Individuals with hand paralysis resulting from C6-C7 spinal cord injuries frequently rely on tenodesis for grasping. However, tenodesis generates limited grasping force and demands constant exertion to maintain a grasp, leading to fatigue and sometimes pain. We introduce the MyHand-SCI, a wearable robot that provides grasping assistance through motorized exotendons. Our user-driven device enables independent, ipsilateral operation via a novel Throttle-based Wrist Angle control method, which allows users to maintain grasps without continued wrist extension. A pilot case study with a person with C6 spinal cord injury shows an improvement in functional grasping and grasping force, as well as a preserved ability to modulate grasping force while using our device, thus improving their ability to manipulate everyday objects. This research is a step towards developing effective and intuitive wearable assistive devices for individuals with spinal cord injury.

cs.RO↗

arXiv2023

Towards Tenodesis-Modulated Control of an Assistive Hand Exoskeleton for SCI

Restoration of hand function is one of the highest priorities for SCI populations. In this work, we present a prototype of a robotic assistive orthosis capable of implementing tenodesis user control. The underactuated device provides active grasping assistance while preserving free wrist mobility through the use of Bowden cables. This device enables force modulation during grasping, which was effectively leveraged by a participant with C6 SCI to demonstrate improved grasping abilities using the orthosis, scoring 11 on the Grasp and Release Test using the device compared to 1 without it.

cs.RO↗