221 citations · 935 across the 44 of their papers we have counts for
31 papers · 1 filter
Predicting Routine Object Usage for Proactive Robot Assistance
Maithili Patel, Aswin Prakash, Sonia Chernova
Proactivity in robot assistance refers to the robot's ability to anticipate user needs and perform assistive actions without explicit requests. This requires understanding user rou…
IndoorSim-to-OutdoorReal: Learning to Navigate Outdoors without any Outdoor Experience
Joanne Truong, April Zitkovich, Sonia Chernova +4
We present IndoorSim-to-OutdoorReal (I2O), an end-to-end learned visual navigation approach, trained solely in simulated short-range indoor environments, and demonstrates zero-shot…
Proactive Robot Assistance via Spatio-Temporal Object Modeling
Maithili Patel, Sonia Chernova
Proactive robot assistance enables a robot to anticipate and provide for a user's needs without being explicitly asked. We formulate proactive assistance as the problem of the robo…
StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects
Weiyu Liu, Yilun Du, Tucker Hermans +2
Robots operating in human environments must be able to rearrange objects into semantically-meaningful configurations, even if these objects are previously unseen. In this work, we…
D-ITAGS: A Dynamic Interleaved Approach to Resilient Task Allocation, Scheduling, and Motion Planning
Glen Neville, Sonia Chernova, Harish Ravichandar
Complex, multi-objective missions require the coordination of heterogeneous robots at multiple inter-connected levels, such as coalition formation, scheduling, and motion planning.…
Rethinking Sim2Real: Lower Fidelity Simulation Leads to Higher Sim2Real Transfer in Navigation
Joanne Truong, Max Rudolph, Naoki Yokoyama +3
If we want to train robots in simulation before deploying them in reality, it seems natural and almost self-evident to presume that reducing the sim2real gap involves creating simu…
Semantic-Based Explainable AI: Leveraging Semantic Scene Graphs and Pairwise Ranking to Explain Robot Failures
Devleena Das, Sonia Chernova
When interacting in unstructured human environments, occasional robot failures are inevitable. When such failures occur, everyday people, rather than trained technicians, will be t…
Desperate Times Call for Desperate Measures: Towards Risk-Adaptive Task Allocation
Max Rudolph, Sonia Chernova, Harish Ravichandar
Multi-robot task allocation (MRTA) problems involve optimizing the allocation of robots to tasks. MRTA problems are known to be challenging when tasks require multiple robots and t…
An Interleaved Approach to Trait-Based Task Allocation and Scheduling
Glen Neville, Andrew Messing, Harish Ravichandar +2
To realize effective heterogeneous multi-robot teams, researchers must leverage individual robots' relative strengths and coordinate their individual behaviors. Specifically, heter…
Towards Robust One-shot Task Execution using Knowledge Graph Embeddings
Angel Daruna, Lakshmi Nair, Weiyu Liu +1
Requiring multiple demonstrations of a task plan presents a burden to end-users of robots. However, robustly executing tasks plans from a single end-user demonstration is an ongoin…
Continual Learning of Knowledge Graph Embeddings
Angel Daruna, Mehul Gupta, Mohan Sridharan +1
In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications. Ho…
Towards Coordinated Robot Motions: End-to-End Learning of Motion Policies on Transform Trees
M. Asif Rana, Anqi Li, Dieter Fox +3
Generating robot motion that fulfills multiple tasks simultaneously is challenging due to the geometric constraints imposed by the robot. In this paper, we propose to solve multi-t…
Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping
Adithyavairavan Murali, Weiyu Liu, Kenneth Marino +2
Despite the enormous progress and generalization in robotic grasping in recent years, existing methods have yet to scale and generalize task-oriented grasping to the same extent. T…
Bi-directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents
Joanne Truong, Sonia Chernova, Dhruv Batra
Deep reinforcement learning models are notoriously data hungry, yet real-world data is expensive and time consuming to obtain. The solution that many have turned to is to use simul…
Learning Navigation Skills for Legged Robots with Learned Robot Embeddings
Joanne Truong, Denis Yarats, Tianyu Li +4
Recent work has shown results on learning navigation policies for idealized cylinder agents in simulation and transferring them to real wheeled robots. Deploying such navigation po…
Tool Macgyvering: A Novel Framework for Combining Tool Substitution and Construction
Lakshmi Nair, Nithin Shrivatsav, Sonia Chernova
Macgyvering refers to solving problems inventively by using whatever objects are available at hand. Tool Macgyvering is a subset of macgyvering tasks involving a missing tool that…
Feature Guided Search for Creative Problem Solving Through Tool Construction
Lakshmi Nair, Sonia Chernova
Robots in the real world should be able to adapt to unforeseen circumstances. Particularly in the context of tool use, robots may not have access to the tools they need for complet…
Anticipatory Human-Robot Collaboration via Multi-Objective Trajectory Optimization
Abhinav Jain, Daphne Chen, Dhruva Bansal +6
We address the problem of adapting robot trajectories to improve safety, comfort, and efficiency in human-robot collaborative tasks. To this end, we propose CoMOTO, a trajectory op…
Human-Centric Active Perception for Autonomous Observation
David Kent, Sonia Chernova
As robot autonomy improves, robots are increasingly being considered in the role of autonomous observation systems -- free-flying cameras capable of actively tracking human activit…
Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
Zackory Erickson, Eliot Xing, Bharat Srirangam +2
Material recognition can help inform robots about how to properly interact with and manipulate real-world objects. In this paper, we present a multimodal sensing technique, leverag…
Taking Recoveries to Task: Recovery-Driven Development for Recipe-based Robot Tasks
Siddhartha Banerjee, Angel Daruna, David Kent +9
Robot task execution when situated in real-world environments is fragile. As such, robot architectures must rely on robust error recovery, adding non-trivial complexity to highly-c…
Tool Substitution with Shape and Material Reasoning Using Dual Neural Networks
Nithin Shrivatsav, Lakshmi Nair, Sonia Chernova
This paper explores the problem of tool substitution, namely, identifying substitute tools for performing a task from a given set of candidate tools. We introduce a novel approach…
Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance
M. Asif Rana, Daphne Chen, S. Reza Ahmadzadeh +3
In this work, we contribute a large-scale study benchmarking the performance of multiple motion-based learning from demonstration approaches. Given the number and diversity of exis…
CAGE: Context-Aware Grasping Engine
Weiyu Liu, Angel Daruna, Sonia Chernova
Semantic grasping is the problem of selecting stable grasps that are functionally suitable for specific object manipulation tasks. In order for robots to effectively perform object…