Uploaded September 2022 | Updated September 2026, 3 weeks ago
Training deep reinforcement learning (DRL) locomotion policies often requires massive amounts of data to converge to the desired behavior. In this regard, simulators provide a cheap and abundant source. For successful sim-to-real transfer, exhaustively engineered approaches such as system identification, dynamics randomization, and domain adaptation are generally employed. As an alternative, we investigate a simple strategy of random force injection (RFI) to perturb system dynamics during training. We show that the application of random forces enables us to emulate dynamics randomization. This allows us to obtain locomotion policies that are robust to variations in system dynamics. We further extend RFI, referred to as extended random force injection (ERFI), by introducing an episodic actuation offset. We demonstrate that ERFI provides additional robustness for variations in system mass offering on average a 61% improved performance over RFI. We also show that ERFI is sufficient to perform a successful sim-to-real transfer on two different quadrupedal platforms, ANYmal C and Unitree A1, even for perceptive locomotion over uneven terrain in outdoor environments.
Authors: Luigi Campanaro, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis
Pre-print: arxiv.org/abs/2209.12878
Website: sites.google.com/view/erfi-icra
Training deep reinforcement learning (DRL) locomotion policies often requires massive amounts of data to converge to the desired behavior. In this regard, simulators provide a cheap and abundant source. For successful sim-to-real transfer, exhaustively engineered approaches such as system identification, dynamics randomization, and domain adaptation are generally employed. As an alternative, we investigate a simple strategy of random force injection (RFI) to perturb system dynamics during training. We show that the application of random forces enables us to emulate dynamics randomization. This allows us to obtain locomotion policies that are robust to variations in system dynamics. We further extend RFI, referred to as extended random force injection (ERFI), by introducing an episodic actuation offset. We demonstrate that ERFI provides additional robustness for variations in system mass offering on average a 61% improved performance over RFI. We also show that ERFI is sufficient to perform a successful sim-to-real transfer on two different quadrupedal platforms, ANYmal C and Unitree A1, even for perceptive locomotion over uneven terrain in outdoor environments.
Authors: Luigi Campanaro, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis
Pre-print: arxiv.org/abs/2209.12878
Website: sites.google.com/view/erfi-icra
![RA-L/ICRA 2021 - Unified Landmark Tracking for Odometry [Finalist ICRA Best Student Paper]](https://i.ytimg.com/vi/l3XSGIpQP3Q/mqdefault.jpg "RA-L/ICRA 2021 - Unified Landmark Tracking for Odometry [Finalist ICRA Best Student Paper]
RA-L/ICRA 2021
Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry
David Wisth, Marco Camurri, Sandipan Das, Maurice Fallon
Paper link: https://arxiv.org/abs/2011.06838
VILENS website: https://ori.ox.ac.uk/labs/drs/vilens-tightly-fused-multi-sensor-odometry/
Abstract:
We present an efficient multi-sensor odometry system
for mobile platforms that jointly optimizes visual, lidar, and
inertial information within a single integrated factor graph. This
runs in real-time at full framerate using fixed lag smoothing.
To perform such tight integration, a new method to extract 3D
line and planar primitives from lidar point clouds is presented.
This approach overcomes the suboptimality of typical frame-to-
frame tracking methods by treating the primitives as landmarks
and tracking them over multiple scans. True integration of
lidar features with standard visual features and IMU is made
possible using a subtle passive synchronization of lidar and
camera frames. The lightweight formulation of the 3D features
allows for real-time execution on a single CPU. Our proposed
system has been tested on a variety of platforms and scenarios,
including underground exploration with a legged robot and
outdoor scanning with a dynamically moving handheld device,
for a total duration of 96 min and 2.4 km traveled distance. In
these test sequences, using only one exteroceptive sensor leads
to failure due to either underconstrained geometry (affecting
lidar) or textureless areas caused by aggressive lighting changes
(affecting vision). In these conditions, our factor graph naturally
uses the best information available from each sensor modality
without any hard switches.")
![[Presentation] Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks](https://i.ytimg.com/vi/mNMdlNkWeVM/mqdefault.jpg "[Presentation] Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks
Video presentation of the article https://arxiv.org/abs/2010.09232
Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks
Yiduo Wang, Nils Funk, Milad Ramezani, Sotiris Papatheodorou, Marija Popovic, Marco Camurri, Stefan Leutenegger, Maurice Fallon
IEEE International Conference on Robotics and Automation (ICRA), 2021
Abstract:
We present an efficient, elastic 3D LiDAR reconstruction framework which can reconstruct up to maximum LiDAR ranges (60 m) at multiple frames per second, thus enabling robot exploration in large-scale environments. Our approach only requires a CPU. We focus on three main challenges of large-scale reconstruction: integration of long-range LiDAR scans at high frequency, the capacity to deform the reconstruction after loop closures are detected, and scalability for long-duration exploration. Our system extends upon a state-of-the-art efficient RGB-D volumetric reconstruction technique, called supereight, to support LiDAR scans and a newly developed submapping technique to allow for dynamic correction of the 3D reconstruction. We then introduce a novel pose graph clustering and submap fusion feature to make the proposed system more scalable for large environments. We evaluate the performance using two public datasets including outdoor exploration with a handheld device and a drone, and with a mobile robot exploring an underground room network. Experimental results demonstrate that our system can reconstruct at 3 Hz with 60 m sensor range and ~5 cm resolution, while state-of-the-art approaches can only reconstruct to 25 cm resolution or 20 m range at the same frequency.")
