Publications

In autonomous navigation settings, several quantities can be subject to variations. Terrain properties such as friction coefficients may vary over time depending on the location of the robot. Also, the dynamics of the robot may change due to, e.g., different payloads, changing the system's mass, or wear and tear, changing actuator gains or joint friction. An autonomous agent should thus be able to adapt to such variations. In this paper, we develop a novel probabilistic, terrain- and robot-aware forward dynamics model, termed TRADYN, which is able to adapt to the above-mentioned variations. It builds on recent advances in meta-learning forward dynamics models based on Neural Processes. We evaluate our method in a simulated 2D navigation setting with a unicycle-like robot and different terrain layouts with spatially varying friction coefficients. In our experiments, the proposed model exhibits lower prediction error for the task of long-horizon trajectory prediction, compared to non-adaptive ablation models. We also evaluate our model on the downstream task of navigation planning, which demonstrates improved performance in planning control-efficient paths by taking robot and terrain properties into account.

Intelligent robots require object-level scene understanding to reason about possible tasks and interactions with the environment. Moreover, many perception tasks such as scene reconstruction, image retrieval, or place recognition can benefit from reasoning on the level of objects. While keypoint-based matching can yield strong results for finding correspondences for images with small to medium view point changes, for large view point changes, matching semantically on the object-level becomes advantageous. In this paper, we propose a learning-based approach which combines local keypoints with novel object-level features for matching object detections between RGB images. We train our object-level matching features based on appearance and inter-frame and cross-frame spatial relations between objects in an associative graph neural network. We demonstrate our approach in a large variety of views on realistically rendered synthetic images. Our approach compares favorably to previous state-of-the-art object-level matching approaches and achieves improved performance over a pure keypoint-based approach for large view-point changes.

Humans have the remarkable ability to perceive and interact with objects in the world around them. They can easily segment objects from visual data and have an intuitive understanding of how physics influences objects. By contrast, robots are so far often constrained to tailored environments for a specific task, due to their inability to reconstruct a versatile and accurate scene representation. In this thesis, we combine RGB-D video data with background knowledge of real-world physics to develop such a representation for robots.</br> </br> Our contributions can be separated into two main parts: a dynamic object tracking tool and optimization frameworks that allow for improving shape reconstructions based on physical plausibility. The dynamic object tracking tool "EM-Fusion" detects, segments, reconstructs, and tracks objects from RGB-D video data. We propose a probabilistic data association approach for attributing the image pixels to the different moving objects in the scene. This allows us to track and reconstruct moving objects and the background scene with state-of-the art accuracy and robustness towards occlusions.</br> </br> We investigate two ways of further optimizing the reconstructed shapes of moving objects based on physical plausibility. The first of these, "Co-Section", includes physical plausibility by reasoning about the empty space around an object. We observe that no two objects can occupy the same space at the same time and that the depth images in the input video provide an estimate of observed empty space. Based on these observations, we propose intersection and hull constraints, which we combine with the observed surfaces in a global optimization approach. Compared to EM-Fusion, which only reconstructs the observed surface, Co-Section optimizes watertight shapes. These watertight shapes provide a rough estimate of unseen surfaces and could be useful as initialization for further refinement, e.g., by interactive perception. In the second optimization approach, "DiffSDFSim", we reason about object shapes based on physically plausible object motion. We observe that object trajectories after collisions depend on the object's shape, and extend a differentiable physics simulation for optimizing object shapes together with other physical properties (e.g., forces, masses, friction) based on the motion of the objects and their interactions. Our key contributions are using signed distance function models for representing shapes and a novel method for computing gradients that models the dependency of the time of contact on object shapes. We demonstrate that our approach recovers target shapes well by fitting to target trajectories and depth observations. Further, the ground-truth trajectories are recovered well in simulation using the resulting shape and physical properties. This enables predictions about the future motion of objects by physical simulation.</br> </br> We anticipate that our contributions can be useful building blocks in the development of 3D environment perception for robots. The reconstruction of individual objects as in EM-Fusion is a key ingredient required for interactions with objects. Completed shapes as the ones provided by Co-Section provide useful cues for planning interactions like grasping of objects. Finally, the recovery of shape and other physical parameters using differentiable simulation as in DiffSDFSim allows simulating objects and thus predicting the effects of interactions. Future work might extend the presented works for interactive perception of dynamic environments by comparing these predictions with observed real-world interactions to further improve the reconstructions and physical parameter estimations.

Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art approaches fail to produce reliable estimation of the robot posture, in particular base height. In this paper, we propose a novel approach for combining visual-inertial odometry (VIO) with leg odometry in an extended Kalman filter (EKF) based state estimator. The VIO module uses a stereo camera and IMU to yield low-drift 3D position and yaw orientation and drift-free pitch and roll orientation of the robot base link in the inertial frame. However, these values have a considerable amount of latency due to image processing and optimization, while the rate of update is quite low which is not suitable for low-level control. To reduce the latency, we predict the VIO state estimate at the rate of the IMU measurements of the VIO sensor. The EKF module uses the base pose and linear velocity predicted by VIO, fuses them further with a second high-rate IMU and leg odometry measurements, and produces robot state estimates with a high frequency and small latency suitable for control. We integrate this lightweight estimation framework with a nonlinear model predictive controller and show successful implementation of a set of agile locomotion behaviors, including trotting and jumping at varying horizontal speeds, on a torque-controlled quadruped robot.