Timezone: »
Poster
Learning Calibratable Policies using Programmatic Style-Consistency
Eric Zhan · Albert Tseng · Yisong Yue · Adith Swaminathan · Matthew Hausknecht
Wed Jul 15 10:00 AM -- 10:45 AM & Wed Jul 15 09:00 PM -- 09:45 PM (PDT) @
We study the problem of controllable generation of long-term sequential behaviors, where the goal is to calibrate to multiple behavior styles simultaneously. In contrast to the well-studied areas of controllable generation of images, text, and speech, there are two questions that pose significant challenges when generating long-term behaviors: how should we specify the factors of variation to control, and how can we ensure that the generated behavior faithfully demonstrates combinatorially many styles? We leverage programmatic labeling functions to specify controllable styles, and derive a formal notion of style-consistency as a learning objective, which can then be solved using conventional policy learning approaches. We evaluate our framework using demonstrations from professional basketball players and agents in the MuJoCo physics environment, and show that existing approaches that do not explicitly enforce style-consistency fail to generate diverse behaviors whereas our learned policies can be calibrated for up to $4^5 (1024)$ distinct style combinations.
Author Information
Eric Zhan (California Institute of Technology)
Albert Tseng (Caltech)
Yisong Yue (Caltech)

Yisong Yue is a Professor of Computing and Mathematical Sciences at Caltech and (via sabbatical) a Principal Scientist at Latitude AI. His research interests span both fundamental and applied pursuits, from novel learning-theoretic frameworks all the way to deep learning deployed in autonomous driving on public roads. His work has been recognized with multiple paper awards and nominations, including in robotics, computer vision, sports analytics, machine learning for health, and information retrieval. At Latitude AI, he is working on machine learning approaches to motion planning for autonomous driving.
Adith Swaminathan (Microsoft Research)
Matthew Hausknecht (Microsoft Research)
More from the Same Authors
-
2023 : Towards Modular Machine Learning Pipelines »
Aditya Modi · JIVAT NEET KAUR · Maggie Makar · Pavan Mallapragada · Amit Sharma · Emre Kiciman · Adith Swaminathan -
2023 : Preferential Multi-Attribute Bayesian Optimization with Application to Exoskeleton Personalization »
Raul Astudillo · Amy Li · Maegan Tucker · Chu Xin Cheng · Aaron Ames · Yisong Yue -
2023 : Dueling Bandits for Online Preference Learning »
Yisong Yue -
2023 Poster: Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation »
Fengxue Zhang · Jialin Song · James Bowden · Alexander Ladd · Yisong Yue · Thomas Desautels · Yuxin Chen -
2023 Poster: MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior »
Jennifer J. Sun · Markus Marks · Andrew Ulmer · Dipam Chakraborty · Brian Geuther · Edward Hayes · Heng Jia · Vivek Kumar · Sebastian Oleszko · Zachary Partridge · Milan Peelman · Alice Robie · Catherine Schretter · Keith Sheppard · Chao Sun · Param Uttarwar · Julian Wagner · Erik Werner · Joseph Parker · Pietro Perona · Yisong Yue · Kristin Branson · Ann Kennedy -
2023 Poster: Eventual Discounting Temporal Logic Counterfactual Experience Replay »
Cameron Voloshin · Abhinav Verma · Yisong Yue -
2023 Poster: Hindsight Learning for MDPs with Exogenous Inputs »
Sean R. Sinclair · Felipe Vieira Frujeri · Ching-An Cheng · Luke Marshall · Hugo Barbalho · Jingling Li · Jennifer Neville · Ishai Menache · Adith Swaminathan -
2022 Workshop: Adaptive Experimental Design and Active Learning in the Real World »
Mojmir Mutny · Willie Neiswanger · Ilija Bogunovic · Stefano Ermon · Yisong Yue · Andreas Krause -
2022 Poster: Investigating Generalization by Controlling Normalized Margin »
Alexander Farhang · Jeremy Bernstein · Kushal Tirumala · Yang Liu · Yisong Yue -
2022 Spotlight: Investigating Generalization by Controlling Normalized Margin »
Alexander Farhang · Jeremy Bernstein · Kushal Tirumala · Yang Liu · Yisong Yue -
2022 Poster: LyaNet: A Lyapunov Framework for Training Neural ODEs »
Ivan Dario Jimenez Rodriguez · Aaron Ames · Yisong Yue -
2022 Spotlight: LyaNet: A Lyapunov Framework for Training Neural ODEs »
Ivan Dario Jimenez Rodriguez · Aaron Ames · Yisong Yue -
2021 : Personalized Preference Learning - from Spinal Cord Stimulation to Exoskeletons »
Yisong Yue -
2021 Poster: Learning by Turning: Neural Architecture Aware Optimisation »
Yang Liu · Jeremy Bernstein · Markus Meister · Yisong Yue -
2021 Spotlight: Learning by Turning: Neural Architecture Aware Optimisation »
Yang Liu · Jeremy Bernstein · Markus Meister · Yisong Yue -
2020 Workshop: Real World Experiment Design and Active Learning »
Ilija Bogunovic · Willie Neiswanger · Yisong Yue -
2020 Poster: Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis »
Jung Yeon Park · Kenneth Carr · Stephan Zheng · Yisong Yue · Rose Yu -
2020 Poster: Working Memory Graphs »
Ricky Loynd · Roland Fernandez · Asli Celikyilmaz · Adith Swaminathan · Matthew Hausknecht -
2019 Workshop: Real-world Sequential Decision Making: Reinforcement Learning and Beyond »
Hoang Le · Yisong Yue · Adith Swaminathan · Byron Boots · Ching-An Cheng -
2019 Poster: Batch Policy Learning under Constraints »
Hoang Le · Cameron Voloshin · Yisong Yue -
2019 Oral: Batch Policy Learning under Constraints »
Hoang Le · Cameron Voloshin · Yisong Yue -
2019 Poster: Control Regularization for Reduced Variance Reinforcement Learning »
Richard Cheng · Abhinav Verma · Gabor Orosz · Swarat Chaudhuri · Yisong Yue · Joel Burdick -
2019 Oral: Control Regularization for Reduced Variance Reinforcement Learning »
Richard Cheng · Abhinav Verma · Gabor Orosz · Swarat Chaudhuri · Yisong Yue · Joel Burdick -
2018 Poster: Iterative Amortized Inference »
Joe Marino · Yisong Yue · Stephan Mandt -
2018 Poster: Hierarchical Imitation and Reinforcement Learning »
Hoang Le · Nan Jiang · Alekh Agarwal · Miroslav Dudik · Yisong Yue · Hal Daumé III -
2018 Oral: Iterative Amortized Inference »
Joe Marino · Yisong Yue · Stephan Mandt -
2018 Oral: Hierarchical Imitation and Reinforcement Learning »
Hoang Le · Nan Jiang · Alekh Agarwal · Miroslav Dudik · Yisong Yue · Hal Daumé III -
2018 Poster: Stagewise Safe Bayesian Optimization with Gaussian Processes »
Yanan Sui · Vincent Zhuang · Joel Burdick · Yisong Yue -
2018 Oral: Stagewise Safe Bayesian Optimization with Gaussian Processes »
Yanan Sui · Vincent Zhuang · Joel Burdick · Yisong Yue -
2018 Tutorial: Imitation Learning »
Yisong Yue · Hoang Le -
2017 Poster: Coordinated Multi-Agent Imitation Learning »
Hoang Le · Yisong Yue · Peter Carr · Patrick Lucey -
2017 Talk: Coordinated Multi-Agent Imitation Learning »
Hoang Le · Yisong Yue · Peter Carr · Patrick Lucey