Robotics 7
♻ ★ Robust Robot Walker: Learning Agile Locomotion over Tiny Traps
Quadruped robots must exhibit robust walking capabilities in practical
applications. In this work, we propose a novel approach that enables quadruped
robots to pass various small obstacles, or "tiny traps". Existing methods often
rely on exteroceptive sensors, which can be unreliable for detecting such tiny
traps. To overcome this limitation, our approach focuses solely on
proprioceptive inputs. We introduce a two-stage training framework
incorporating a contact encoder and a classification head to learn implicit
representations of different traps. Additionally, we design a set of tailored
reward functions to improve both the stability of training and the ease of
deployment for goal-tracking tasks. To benefit further research, we design a
new benchmark for tiny trap task. Extensive experiments in both simulation and
real-world settings demonstrate the effectiveness and robustness of our method.
Project Page: https://robust-robot-walker.github.io/
comment: 10 pages, 17 figures
♻ ☆ Auto-Multilift: Distributed Learning and Control for Cooperative Load Transportation With Quadrotors
Designing motion control and planning algorithms for multilift systems
remains challenging due to the complexities of dynamics, collision avoidance,
actuator limits, and scalability. Existing methods that use optimization and
distributed techniques effectively address these constraints and scalability
issues. However, they often require substantial manual tuning, leading to
suboptimal performance. This paper proposes Auto-Multilift, a novel framework
that automates the tuning of model predictive controllers (MPCs) for multilift
systems. We model the MPC cost functions with deep neural networks (DNNs),
enabling fast online adaptation to various scenarios. We develop a distributed
policy gradient algorithm to train these DNNs efficiently in a closed-loop
manner. Central to our algorithm is distributed sensitivity propagation, which
is built on fully exploiting the unique dynamic couplings within the multilift
system. It parallelizes gradient computation across quadrotors and focuses on
actual system state sensitivities relative to key MPC parameters. Extensive
simulations demonstrate favorable scalability to a large number of quadrotors.
Our method outperforms a state-of-the-art open-loop MPC tuning approach by
effectively learning adaptive MPCs from trajectory tracking errors. It also
excels in learning an adaptive reference for reconfiguring the system when
traversing multiple narrow slots.
♻ ☆ SIS: Seam-Informed Strategy for T-shirt Unfolding
Xuzhao Huang, Akira Seino, Fuyuki Tokuda, Akinari Kobayashi, Dayuan Chen, Yasuhisa Hirata, Norman C. Tien, Kazuhiro Kosuge
Seams are information-rich components of garments. The presence of different
types of seams and their combinations helps to select grasping points for
garment handling. In this paper, we propose a new Seam-Informed Strategy (SIS)
for finding actions for handling a garment, such as grasping and unfolding a
T-shirt. Candidates for a pair of grasping points for a dual-arm manipulator
system are extracted using the proposed Seam Feature Extraction Method (SFEM).
A pair of grasping points for the robot system is selected by the proposed
Decision Matrix Iteration Method (DMIM). The decision matrix is first computed
by multiple human demonstrations and updated by the robot execution results to
improve the grasping and unfolding performance of the robot. Note that the
proposed scheme is trained on real data without relying on simulation.
Experimental results demonstrate the effectiveness of the proposed strategy.
The project video is available at https://github.com/lancexz/sis.
comment: 8 pages, 8 figures
♻ ☆ GOPT: Generalizable Online 3D Bin Packing via Transformer-based Deep Reinforcement Learning
Robotic object packing has broad practical applications in the logistics and
automation industry, often formulated by researchers as the online 3D Bin
Packing Problem (3D-BPP). However, existing DRL-based methods primarily focus
on enhancing performance in limited packing environments while neglecting the
ability to generalize across multiple environments characterized by different
bin dimensions. To this end, we propose GOPT, a generalizable online 3D Bin
Packing approach via Transformer-based deep reinforcement learning (DRL).
First, we design a Placement Generator module to yield finite subspaces as
placement candidates and the representation of the bin. Second, we propose a
Packing Transformer, which fuses the features of the items and bin, to identify
the spatial correlation between the item to be packed and available sub-spaces
within the bin. Coupling these two components enables GOPT's ability to perform
inference on bins of varying dimensions. We conduct extensive experiments and
demonstrate that GOPT not only achieves superior performance against the
baselines, but also exhibits excellent generalization capabilities.
Furthermore, the deployment with a robot showcases the practical applicability
of our method in the real world. The source code will be publicly available at
https://github.com/Xiong5Heng/GOPT.
comment: 8 pages, 6 figures. This paper has been accepted by IEEE Robotics and
Automation Letters
♻ ☆ Introducing a Class-Aware Metric for Monocular Depth Estimation: An Automotive Perspective ECCV
The increasing accuracy reports of metric monocular depth estimation models
lead to a growing interest from the automotive domain. Current model
evaluations do not provide deeper insights into the models' performance, also
in relation to safety-critical or unseen classes. Within this paper, we present
a novel approach for the evaluation of depth estimation models. Our proposed
metric leverages three components, a class-wise component, an edge and corner
image feature component, and a global consistency retaining component. Classes
are further weighted on their distance in the scene and on criticality for
automotive applications. In the evaluation, we present the benefits of our
metric through comparison to classical metrics, class-wise analytics, and the
retrieval of critical situations. The results show that our metric provides
deeper insights into model results while fulfilling safety-critical
requirements. We release the code and weights on the following repository:
https://github.com/leisemann/ca_mmde
comment: Accepted at the European Conference on Computer Vision (ECCV) 2024
Workshop on Out Of Distribution Generalization in Computer Vision
♻ ☆ Tightly-Coupled LiDAR-IMU-Wheel Odometry with Online Calibration of a Kinematic Model for Skid-Steering Robots
Taku Okawara, Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno, Kentaro Uno, Kazuya Yoshida
Tunnels and long corridors are challenging environments for mobile robots
because a LiDAR point cloud should degenerate in these environments. To tackle
point cloud degeneration, this study presents a tightly-coupled LiDAR-IMU-wheel
odometry algorithm with an online calibration for skid-steering robots. We
propose a full linear wheel odometry factor, which not only serves as a motion
constraint but also performs the online calibration of kinematic models for
skid-steering robots. Despite the dynamically changing kinematic model (e.g.,
wheel radii changes caused by tire pressures) and terrain conditions, our
method can address the model error via online calibration. Moreover, our method
enables an accurate localization in cases of degenerated environments, such as
long and straight corridors, by calibration while the LiDAR-IMU fusion
sufficiently operates. Furthermore, we estimate the uncertainty (i.e.,
covariance matrix) of the wheel odometry online for creating a reasonable
constraint. The proposed method is validated through three experiments. The
first indoor experiment shows that the proposed method is robust in severe
degeneracy cases (long corridors) and changes in the wheel radii. The second
outdoor experiment demonstrates that our method accurately estimates the sensor
trajectory despite being in rough outdoor terrain owing to online uncertainty
estimation of wheel odometry. The third experiment shows the proposed online
calibration enables robust odometry estimation in changing terrains.
comment: Accepted by IEEE Access journal (12 September) open-source:
https://github.com/TakuOkawara/full_linear_wheel_odometry_factor
♻ ☆ BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous Driving
Current research in semantic bird's-eye view segmentation for autonomous
driving focuses solely on optimizing neural network models using a single
dataset, typically nuScenes. This practice leads to the development of highly
specialized models that may fail when faced with different environments or
sensor setups, a problem known as domain shift. In this paper, we conduct a
comprehensive cross-dataset evaluation of state-of-the-art BEV segmentation
models to assess their performance across different training and testing
datasets and setups, as well as different semantic categories. We investigate
the influence of different sensors, such as cameras and LiDAR, on the models'
ability to generalize to diverse conditions and scenarios. Additionally, we
conduct multi-dataset training experiments that improve models' BEV
segmentation performance compared to single-dataset training. Our work
addresses the gap in evaluating BEV segmentation models under cross-dataset
validation. And our findings underscore the importance of enhancing model
generalizability and adaptability to ensure more robust and reliable BEV
segmentation approaches for autonomous driving applications. The code for this
paper available at https://github.com/manueldiaz96/beval .