4 citations · 11 across the 9 of their papers we have counts for
12 papers
Video2SwimFish: An Automated Pipeline for Reconstructing Controllable Fish Models and Biological Locomotion from Real Fish Videos
Hangong Chen, Linfeng Cheng, Tahsin Zaman Jilan +4
We present Video2SwimFish, an automated pipeline and benchmark for building controllable fish assets from real-fish videos for underwater embodied AI. Given synchronized multi-view…
NatSGLD: A Dataset with Speech, Gesture, Logic, and Demonstration for Robot Learning in Natural Human-Robot Interaction
Snehesh Shrestha, Yantian Zha, Saketh Banagiri +3
Recent advances in multimodal Human-Robot Interaction (HRI) datasets emphasize the integration of speech and gestures, allowing robots to absorb explicit knowledge and tacit unders…
AAM-SEALS: Developing Aerial-Aquatic Manipulators in SEa, Air, and Land Simulator
William Yang, Karthikeya Kona, Yashveer Jain +4
Current mobile manipulators and high-fidelity simulators lack the ability to seamlessly operate and simulate across integrated environments spanning sea, air, and land. To address…
NatSGD: A Dataset with Speech, Gestures, and Demonstrations for Robot Learning in Natural Human-Robot Interaction
Snehesh Shrestha, Yantian Zha, Saketh Banagiri +3
Recent advancements in multimodal Human-Robot Interaction (HRI) datasets have highlighted the fusion of speech and gesture, expanding robots' capabilities to absorb explicit and im…
Task Success is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors
Lin Guan, Yifan Zhou, Denis Liu +3
Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences. Their full power is…
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning
Yantian Zha, Lin Guan, Subbarao Kambhampati
Our work aims at efficiently leveraging ambiguous demonstrations for the training of a reinforcement learning (RL) agent. An ambiguous demonstration can usually be interpreted in m…
Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems
Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma +2
Despite the surprising power of many modern AI systems that often learn their own representations, there is significant discontent about their inscrutability and the attendant prob…
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping
Yantian Zha, Siddhant Bhambri, Lin Guan
Conventional works that learn grasping affordance from demonstrations need to explicitly predict grasping configurations, such as gripper approaching angles or grasping preshapes.…
Plan-Recognition-Driven Attention Modeling for Visual Recognition
Yantian Zha, Yikang Li, Tianshu Yu +2
Human visual recognition of activities or external agents involves an interplay between high-level plan recognition and low-level perception. Given that, a natural question to ask…
Discovering Underlying Plans Based on Shallow Models
Hankz Hankui Zhuo, Yantian Zha, Subbarao Kambhampati
Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or domain models in hand. Previous approaches eithe…
Recognizing Plans by Learning Embeddings from Observed Action Distributions
Yantian Zha, Yikang Li, Sriram Gopalakrishnan +2
Recent advances in visual activity recognition have raised the possibility of applications such as automated video surveillance. Effective approaches for such problems however requ…
Explicablility as Minimizing Distance from Expected Behavior
Anagha Kulkarni, Yantian Zha, Tathagata Chakraborti +3
In order to have effective human-AI collaboration, it is necessary to address how the AI agent's behavior is being perceived by the humans-in-the-loop. When the agent's task plans…