Public articles linked to the same research event.
arXiv The authors propose Risk-Aware Tactile Encoding (RATE): bilateral tactile history is aggregated by a two-layer LSTM into interaction context, shaped jointly by short-horizon future contact-change prediction and continuous task-conditioned alert supervision, then added to a frozen pretrained ACT policy through a zero-initialized residual adapter that updates about 0.59% of parameters; RATE reaches 71.8% macro-average success on the eight UniVTAC simulation tasks (retrained ACT+UniVTAC: 47.4%) and, on real-world USB Insertion, Bottle Cap Screwing, and Plug Insertion, achieves success rates of 75%, 85%, and 60% with contact-safe success rates of 93.33%, 100%, and 83.33%.
The authors propose Risk-Aware Tactile Encoding (RATE): bilateral tactile history is aggregated by a two-layer LSTM into interaction context, shaped jointly by short-horizon future contact-change prediction and continuous task-conditioned alert supervision, then added to a frozen pretrained ACT policy through a zero-initialized residual adapter that updates about 0.59% of parameters; RATE reaches 71.8% macro-average success on the eight UniVTAC simulation tasks (retrained ACT+UniVTAC: 47.4%) and, on real-world USB Insertion, Bottle Cap Screwing, and Plug Insertion, achieves success rates of 75%, 85%, and 60% with contact-safe success rates of 93.33%, 100%, and 83.33%.
The authors propose Risk-Aware Tactile Encoding (RATE): bilateral tactile history is aggregated by a two-layer LSTM into interaction context, shaped jointly by short-horizon future contact-change prediction and continuous task-conditioned alert supervision, then added to a frozen pretrained ACT policy through a zero-initialized residual adapter that updates about 0.59% of parameters; RATE reaches 71.8% macro-average success on the eight UniVTAC simulation tasks (retrained ACT+UniVTAC: 47.4%) and, on real-world USB Insertion, Bottle Cap Screwing, and Plug Insertion, achieves success rates of 75%, 85%, and 60% with contact-safe success rates of 93.33%, 100%, and 83.33%.
The authors propose Risk-Aware Tactile Encoding (RATE): bilateral tactile history is aggregated by a two-layer LSTM into interaction context, shaped jointly by short-horizon future contact-change prediction and continuous task-conditioned alert supervision, then added to a frozen pretrained ACT policy through a zero-initialized residual adapter that updates about 0.59% of parameters; RATE reaches 71.8% macro-average success on the eight UniVTAC simulation tasks (retrained ACT+UniVTAC: 47.4%) and, on real-world USB Insertion, Bottle Cap Screwing, and Plug Insertion, achieves success rates of 75%, 85%, and 60% with contact-safe success rates of 93.33%, 100%, and 83.33%.