23 citations · 46 across the 6 of their papers we have counts for
6 papers
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving
Kalle Kujanpää, Daulet Baimukashev, Farzeen Munir +4
Learning to perform accurate and rich simulations of human driving behaviors from data for autonomous vehicle testing remains challenging due to human driving styles' high diversit…
Learning Transparent Reward Models via Unsupervised Feature Selection
Daulet Baimukashev, Gokhan Alcan, Kevin Sebastian Luck +1
In complex real-world tasks such as robotic manipulation and autonomous driving, collecting expert demonstrations is often more straightforward than specifying precise learning obj…
Automated Feature Selection for Inverse Reinforcement Learning
Daulet Baimukashev, Gokhan Alcan, Ville Kyrki
Inverse reinforcement learning (IRL) is an imitation learning approach to learning reward functions from expert demonstrations. Its use avoids the difficult and tedious procedure o…
Challenges of Data-Driven Simulation of Diverse and Consistent Human Driving Behaviors
Kalle Kujanpää, Daulet Baimukashev, Shibei Zhu +4
Building simulation environments for developing and testing autonomous vehicles necessitates that the simulators accurately model the statistical realism of the real-world environm…
End-to-End Deep Fault Tolerant Control
Daulet Baimukashev, Bexultan Rakhim, Matteo Rubagotti +1
PUBLISHED ON IEEE/ASME TRANSACTIONS ON MECHATRONICS, DOI: 10.1109/TMECH.2021.3100150. Ideally, accurate sensor measurements are needed to achieve a good performance in the closed-l…
Color-Coded Fiber-Optic Tactile Sensor for an Elastomeric Robot Skin
Zhanat Kappassov, Daulet Baimukashev, Zharaskhan Kuanyshuly +3
The sense of touch is essential for reliable mapping between the environment and a robot which interacts physically with objects. Presumably, an artificial tactile skin would facil…