Haptic SLAM for context-aware robotic hand prosthetics - simultaneous inference of hand pose and object shape using particle filters. - Feryal M. P. Behbahani; Ruth Taunton; Andreas A. C. Thomik; A. Aldo Faisal (NER, 2015)

Haptic SLAM: An Ideal Observer Model for Bayesian Inference of Object Shape and Hand Pose from Contact Dynamics. - Feryal M. P. Behbahani; Guillem Singla-Buxarrais; A. Aldo Faisal (EuroHaptics (1), 2016)

Reverse-engineering the visual and haptic perceptual algorithms in the brain. - Feryal M. P. Behbahani (2016)

Extending World Models for Multi-Agent Reinforcement Learning in MALMÖ. - Valliappa Chockalingam; Tegg Tae Kyong Sung; Feryal M. P. Behbahani; Rishab Gargeya; Amlesh Sivanantham; Aleksandra Malysheva (AIIDE Workshops, 2018)

Learning From Demonstration in the Wild. - Feryal M. P. Behbahani; Kyriacos Shiarlis; Xi Chen; Vitaly Kurin; Sudhanshu Kasewa; Ciprian Stirbu; João Gomes; Supratik Paul; Frans A. Oliehoek; João V. Messias; Shimon Whiteson (ICRA, 2019)

Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation. - Tianhong Dai; Kai Arulkumaran; Samyakh Tukra; Feryal M. P. Behbahani; Anil Anthony Bharath (CoRR, 2019)

Modular Meta-Learning with Shrinkage. - Yutian Chen 001; Abram L. Friesen; Feryal M. P. Behbahani; Arnaud Doucet; David Budden; Matthew Hoffman 002; Nando de Freitas (NeurIPS, 2020)

Privileged Information Dropout in Reinforcement Learning. - Pierre-Alexandre Kamienny; Kai Arulkumaran; Feryal M. P. Behbahani; Wendelin Boehmer; Shimon Whiteson (CoRR, 2020)

Acme: A Research Framework for Distributed Reinforcement Learning. - Matt Hoffman 001; Bobak Shahriari; John Aslanides; Gabriel Barth-Maron; Feryal M. P. Behbahani; Tamara Norman; Abbas Abdolmaleki; Albin Cassirer; Fan Yang; Kate Baumli; Sarah Henderson; Alexander Novikov 001; et al. (CoRR, 2020)

Learning Compositional Neural Programs for Continuous Control. - Thomas Pierrot; Nicolas Perrin 001; Feryal M. P. Behbahani; Alexandre Laterre; Olivier Sigaud; Karim Beguir; Nando de Freitas (CoRR, 2020)

On the role of planning in model-based deep reinforcement learning. - Jessica B. Hamrick; Abram L. Friesen; Feryal M. P. Behbahani; Arthur Guez; Fabio Viola; Sims Witherspoon; Thomas Anthony 001; Lars Holger Buesing; Petar Velickovic; Theophane Weber (ICLR, 2021)

Model-Value Inconsistency as a Signal for Epistemic Uncertainty. - Angelos Filos; Eszter Vértes; Zita Marinho; Gregory Farquhar; Diana Borsa; Abram L. Friesen; Feryal M. P. Behbahani; Tom Schaul; André Barreto 001; Simon Osindero (ICML, 2022)

Analysing deep reinforcement learning agents trained with domain randomisation. - Tianhong Dai; Kai Arulkumaran; Tamara Gerbert; Samyakh Tukra; Feryal M. P. Behbahani; Anil Anthony Bharath (Neurocomputing, 2022)

Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality. - Tom Zahavy; Yannick Schroecker; Feryal M. P. Behbahani; Kate Baumli; Sebastian Flennerhag; Shaobo Hou; Satinder Singh 001 (ICLR, 2023)

Human-Timescale Adaptation in an Open-Ended Task Space. - Jakob Bauer; Kate Baumli; Feryal M. P. Behbahani; Avishkar Bhoopchand; Nathalie Bradley-Schmieg; Michael Chang; Natalie Clay; Adrian Collister; Vibhavari Dasagi; Lucy Gonzalez; Karol Gregor; Edward Hughes 001; et al. (ICML, 2023)

Structured State Space Models for In-Context Reinforcement Learning. - Chris Lu 001; Yannick Schroecker; Albert Gu; Emilio Parisotto; Jakob N. Foerster; Satinder Singh 001; Feryal M. P. Behbahani (NeurIPS, 2023)

Human-Timescale Adaptation in an Open-Ended Task Space. - Adaptive Agent Team; Jakob Bauer; Kate Baumli; Satinder Baveja; Feryal M. P. Behbahani; Avishkar Bhoopchand; Nathalie Bradley-Schmieg; Michael Chang; Natalie Clay; Adrian Collister; Vibhavari Dasagi; Lucy Gonzalez; et al. (CoRR, 2023)

Hierarchical Reinforcement Learning in Complex 3D Environments. - Bernardo Ávila Pires; Feryal M. P. Behbahani; Hubert Soyer; Kyriacos Nikiforou; Thomas Keck; Satinder Singh 001 (CoRR, 2023)

Vision-Language Models as a Source of Rewards. - Kate Baumli; Satinder Baveja; Feryal M. P. Behbahani; Harris Chan; Gheorghe Comanici; Sebastian Flennerhag; Maxime Gazeau; Kristian Holsheimer; Dan Horgan; Michael Laskin; Clare Lyle; Hussain Masoom; et al. (CoRR, 2023)

Position: Open-Endedness is Essential for Artificial Superhuman Intelligence. - Edward Hughes 001; Michael D. Dennis; Jack Parker-Holder; Feryal M. P. Behbahani; Aditi Mavalankar; Yuge Shi; Tom Schaul; Tim Rocktäschel (ICML, 2024)

Genie: Generative Interactive Environments. - Jake Bruce; Michael D. Dennis; Ashley Edwards; Jack Parker-Holder; Yuge Shi; Edward Hughes 001; Matthew Lai; Aditi Mavalankar; Richie Steigerwald; Chris Apps; Yusuf Aytar; Sarah Bechtle; et al. (ICML, 2024)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei Zhang; Bernd Bohnet; Luis Rosias; Stephanie C. Y. Chan; Biao Zhang; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; et al. (NeurIPS, 2024)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei M. Zhang; Bernd Bohnet; Stephanie Chan 003; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; Feryal M. P. Behbahani; Aleksandra Faust; et al. (CoRR, 2024)

Open-Endedness is Essential for Artificial Superhuman Intelligence. - Edward Hughes 001; Michael Dennis 001; Jack Parker-Holder; Feryal M. P. Behbahani; Aditi Mavalankar; Yuge Shi; Tom Schaul; Tim Rocktäschel (CoRR, 2024)

Training Language Models to Self-Correct via Reinforcement Learning. - Aviral Kumar; Vincent Zhuang; Rishabh Agarwal; Yi Su; John D. Co-Reyes; Avi Singh; Kate Baumli; Shariq Iqbal; Colton Bishop; Rebecca Roelofs; Lei M. Zhang; Kay McKinney; et al. (ICLR, 2025)