Caleb Chuck

According to our database, Caleb Chuck authored at least 14 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Granger Causal Interaction Skill Chains.

Caleb Chuck, Kevin Black, Aditya Arjun, Yuke Zhu, Scott Niekum

Trans. Mach. Learn. Res., 2024

RL Zero: Zero-Shot Language to Behaviors without any Supervision.

Harshit Sikchi, Siddhant Agarwal, Pranaya Jajoo, Samyak Parajuli, Caleb Chuck, Max Rudolph, Peter Stone, Amy Zhang, Scott Niekum

CoRR, 2024

SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions.

Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen, Roberto Martín-Martín, Amy Zhang, Scott Niekum, Peter Stone

CoRR, 2024

A Dual Approach to Imitation Learning from Observations with Offline Datasets.

Harshit Sikchi, Caleb Chuck, Amy Zhang, Scott Niekum

CoRR, 2024

Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning.

Caleb Chuck, Carl Qi, Michael J. Munje, Shuozhe Li, Max Rudolph, Chang Shi, Siddhant Agarwal, Harshit Sikchi, Abhinav Peri, Sarthak Dayal, Evan Kuo, Kavan Mehta, Anthony Wang, Peter Stone, Amy Zhang, Scott Niekum

CoRR, 2024

Automated Discovery of Functional Actual Causes in Complex Environments.

Caleb Chuck, Sankaran Vaidyanathan, Stephen Giguere, Amy Zhang, David Jensen, Scott Niekum

CoRR, 2024

Learning Action-based Representations Using Invariance.

Max Rudolph, Caleb Chuck, Kevin Black, Misha Lvovsky, Scott Niekum, Amy Zhang

RLJ, 2024

Gaze Supervision for Mitigating Causal Confusion in Driving Agents.

Abhijat Biswas, Badal Arun Pardhi, Caleb Chuck, Jarrett Holtz, Scott Niekum, Henny Admoni, Alessandro Allievi

Proceedings of the IEEE Intelligent Vehicles Symposium, 2024

2023

Granger-Causal Hierarchical Skill Discovery.

Caleb Chuck, Kevin Black, Aditya Arjun, Yuke Zhu, Scott Niekum

CoRR, 2023

2021

ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory.

Ajinkya Jain, Rudolf Lioutikov, Caleb Chuck, Scott Niekum

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020

Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning.

Caleb Chuck, Supawit Chockchowwat, Scott Niekum

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

2017

Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations.

Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin G. Jamieson, Anca D. Dragan, Ken Goldberg

Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

Statistical data cleaning for deep learning of automation tasks from demonstrations.

Caleb Chuck, Michael Laskey, Sanjay Krishnan, Ruta Joshi, Roy Fox, Ken Goldberg

Proceedings of the 13th IEEE Conference on Automation Science and Engineering, 2017

2016

Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations.

Michael Laskey, Jonathan Lee, Caleb Chuck, David V. Gealy, Wesley Yu-Shu Hsieh, Florian T. Pokorny, Anca D. Dragan, Ken Goldberg

Proceedings of the IEEE International Conference on Automation Science and Engineering, 2016