2 citations · 3 across the 6 of their papers we have counts for
8 papers
ECHO: Embodied Camera Observations of Human Object Carrying
Xuefei Sun, Lorin Achey, Kali Hamilton +4
Embodied and assistive agents must do more than recognize objects: they must reason about where an object belongs given the layout of an environment and the habits of the people wh…
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
Multi-Agent Planning with Spatio-Temporal and Topological Constraints using STL-GO
Sheryl Paul, Vidisha Kudalkar, Anand Balakrishnan +3
Multi-agent planning problems arise in a variety of engineering applications, such as multi-robot wildfire fighting and unmanned aerial inspection in factories. A particular challe…
Worth Remembering: Surprise-Gated Robot Episodic Memory
Nicolas Gorlo, Derek K. Wise, Alberto Speranzon +1
Robots solving generalist tasks need to be able to ground instructions in their past experience, since humans may refer to notable past events when giving a task (e.g., ``Take me t…
Stratifying Reinforcement Learning with Signal Temporal Logic
Justin Curry, Alberto Speranzon
In this paper, we develop a stratification-based semantics for Signal Temporal Logic (STL) in which each atomic predicate is interpreted as a membership test in a stratified space.…
Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy
Aditya Gahlawat, Ahmed Aboudonia, Sandeep Banik +5
Imitation learning (IL) enables autonomous behavior by learning from expert demonstrations. While more sample-efficient than comparative alternatives like reinforcement learning, I…
Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Settings
Rushang Karia, Pulkit Verma, Alberto Speranzon +1
This paper introduces a new approach for continual planning and model learning in relational, non-stationary stochastic environments. Such capabilities are essential for the deploy…
Indoor and Outdoor 3D Scene Graph Generation via Language-Enabled Spatial Ontologies
Jared Strader, Nathan Hughes, William Chen +2
This paper proposes an approach to build 3D scene graphs in arbitrary indoor and outdoor environments. Such extension is challenging; the hierarchy of concepts that describe an out…