27 research outputs found
Penalty-Based Imitation Learning With Cross Semantics Generation Sensor Fusion for Autonomous Driving
In recent times, there has been a growing focus on end-to-end autonomous
driving technologies. This technology involves the replacement of the entire
driving pipeline with a single neural network, which has a simpler structure
and faster inference time. However, while this approach reduces the number of
components in the driving pipeline, it also presents challenges related to
interpretability and safety. For instance, the trained policy may not always
comply with traffic rules, and it is difficult to determine the reason for such
misbehavior due to the lack of intermediate outputs. Additionally, the
successful implementation of autonomous driving technology heavily depends on
the reliable and expedient processing of sensory data to accurately perceive
the surrounding environment. In this paper, we provide penalty-based imitation
learning approach combined with cross semantics generation sensor fusion
technologies (P-CSG) to efficiently integrate multiple modalities of
information and enable the autonomous agent to effectively adhere to traffic
regulations. Our model undergoes evaluation within the Town 05 Long benchmark,
where we observe a remarkable increase in the driving score by more than 12%
when compared to the state-of-the-art (SOTA) model, InterFuser. Notably, our
model achieves this performance enhancement while achieving a 7-fold increase
in inference speed and reducing the model size by approximately 30%. For more
detailed information, including code-based resources, they can be found at
https://hk-zh.github.io/p-csg
What Matters to Enhance Traffic Rule Compliance of Imitation Learning for Automated Driving
More research attention has recently been given to end-to-end autonomous
driving technologies where the entire driving pipeline is replaced with a
single neural network because of its simpler structure and faster inference
time. Despite this appealing approach largely reducing the components in
driving pipeline, its simplicity also leads to interpretability problems and
safety issues arXiv:2003.06404. The trained policy is not always compliant with
the traffic rules and it is also hard to discover the reason for the
misbehavior because of the lack of intermediate outputs. Meanwhile, Sensors are
also critical to autonomous driving's security and feasibility to perceive the
surrounding environment under complex driving scenarios. In this paper, we
proposed P-CSG, a novel penalty-based imitation learning approach with cross
semantics generation sensor fusion technologies to increase the overall
performance of End-to-End Autonomous Driving. We conducted an assessment of our
model's performance using the Town 05 Long benchmark, achieving an impressive
driving score improvement of over 15%. Furthermore, we conducted robustness
evaluations against adversarial attacks like FGSM and Dot attacks, revealing a
substantial increase in robustness compared to baseline models.More detailed
information, such as code-based resources, ablation studies and videos can be
found at https://hk-zh.github.io/p-csg-plus.Comment: 8 pages, 2 figure
Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data
The growing interest in language-conditioned robot manipulation aims to
develop robots capable of understanding and executing complex tasks, with the
objective of enabling robots to interpret language commands and manipulate
objects accordingly. While language-conditioned approaches demonstrate
impressive capabilities for addressing tasks in familiar environments, they
encounter limitations in adapting to unfamiliar environment settings. In this
study, we propose a general-purpose, language-conditioned approach that
combines base skill priors and imitation learning under unstructured data to
enhance the algorithm's generalization in adapting to unfamiliar environments.
We assess our model's performance in both simulated and real-world environments
using a zero-shot setting. In the simulated environment, the proposed approach
surpasses previously reported scores for CALVIN benchmark, especially in the
challenging Zero-Shot Multi-Environment setting. The average completed task
length, indicating the average number of tasks the agent can continuously
complete, improves more than 2.5 times compared to the state-of-the-art method
HULC. In addition, we conduct a zero-shot evaluation of our policy in a
real-world setting, following training exclusively in simulated environments
without additional specific adaptations. In this evaluation, we set up ten
tasks and achieved an average 30% improvement in our approach compared to the
current state-of-the-art approach, demonstrating a high generalization
capability in both simulated environments and the real world. For further
details, including access to our code and videos, please refer to our
supplementary materials
Learning from Symmetry: Meta-Reinforcement Learning with Symmetric Data and Language Instructions
Meta-reinforcement learning (meta-RL) is a promising approach that enables
the agent to learn new tasks quickly. However, most meta-RL algorithms show
poor generalization in multiple-task scenarios due to the insufficient task
information provided only by rewards. Language-conditioned meta-RL improves the
generalization by matching language instructions and the agent's behaviors.
Learning from symmetry is an important form of human learning, therefore,
combining symmetry and language instructions into meta-RL can help improve the
algorithm's generalization and learning efficiency. We thus propose a dual-MDP
meta-reinforcement learning method that enables learning new tasks efficiently
with symmetric data and language instructions. We evaluate our method in
multiple challenging manipulation tasks, and experimental results show our
method can greatly improve the generalization and efficiency of
meta-reinforcement learning