CLAMP: Crowdsourcing a LArge-scale in-the-wild haptic dataset with an open-source device for Multimodal robot Perception

Robust robot manipulation in unstructured environments often requires understanding object properties that extend beyond geometry, such as material or compliance-properties that can be challenging to infer using vision alone. Multimodal haptic sensing provides a promising avenue for inferring such properties, yet progress has been constrained by the lack of large, diverse, and realistic haptic datasets. In this work, we introduce the CLAMP device, a low-cost (<\$200) sensorized reacher-grabber designed to collect large-scale, in-the-wild multimodal haptic data from non-expert users in everyday settings. We deployed 16 CLAMP devices to 41 participants, resulting in the CLAMP dataset, the largest open-source multimodal haptic dataset to date, comprising 12.3 million datapoints across 5357 household objects. Using this dataset, we train a haptic encoder that can infer material and compliance object properties from multimodal haptic data. We leverage this encoder to create the CLAMP model, a visuo-haptic perception model for material recognition that generalizes to novel objects and three robot embodiments with minimal finetuning. We also demonstrate the effectiveness of our model in three real-world robot manipulation tasks: sorting recyclable and non-recyclable waste, retrieving objects from a cluttered bag, and distinguishing overripe from ripe bananas. Our results show that large-scale, in-the-wild haptic data collection can unlock new capabilities for generalizable robot manipulation. Website: https://emprise.cs.cornell.edu/clamp/

GRACE: Generalizing Robot-Assisted Caregiving with User Functionality Embeddings

Robot caregiving should be personalized to meet the diverse needs of care recipients -- assisting with tasks as needed, while taking user agency in action into account. In physical tasks such as handover, bathing, dressing, and rehabilitation, a key aspect of this diversity is the functional range of motion (fROM), which can vary significantly between individuals. In this work, we learn to predict personalized fROM as a way to generalize robot decision-making in a wide range of caregiving tasks. We propose a novel data-driven method for predicting personalized fROM using functional assessment scores from occupational therapy. We develop a neural model that learns to embed functional assessment scores into a latent representation of the user's physical function. The model is trained using motion capture data collected from users with emulated mobility limitations. After training, the model predicts personalized fROM for new users without motion capture. Through simulated experiments and a real-robot user study, we show that the personalized fROM predictions from our model enable the robot to provide personalized and effective assistance while improving the user's agency in action. See our website for more visualizations: https://emprise.cs.cornell.edu/grace/.