1 citations · 2 across the 3 of their papers we have counts for
7 papers
On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Sets
Hyounghun Kim, Aishwarya Padmakumar, Di Jin +2
Natural language guided embodied task completion is a challenging problem since it requires understanding natural language instructions, aligning them with egocentric visual observ…
Generative Conversational Networks
Alexandros Papangelis, Karthik Gopalakrishnan, Aishwarya Padmakumar +3
Inspired by recent work in meta-learning and generative teaching networks, we propose a framework called Generative Conversational Networks, in which conversational agents learn to…
Dialog as a Vehicle for Lifelong Learning
Aishwarya Padmakumar, Raymond J. Mooney
Dialog systems research has primarily been focused around two main types of applications - task-oriented dialog systems that learn to use clarification to aid in understanding a go…
Dialog Policy Learning for Joint Clarification and Active Learning Queries
Aishwarya Padmakumar, Raymond J. Mooney
Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training. Dialog interaction can enable this by the u…
Improving Grounded Natural Language Understanding through Human-Robot Dialog
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov +6
Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an enviro…
Interaction and Autonomy in RoboCup@Home and Building-Wide Intelligence
Justin Hart, Harel Yedidsion, Yuqian Jiang +8
Efforts are underway at UT Austin to build autonomous robot systems that address the challenges of long-term deployments in office environments and of the more prescribed domestic…