Markerless visual servoing on unknown objects for humanoid robot platforms

To precisely reach for an object with a humanoid robot, it is of central importance to have good knowledge of both end-effector, object pose and shape. In this work we propose a framework for markerless visual servoing on unknown objects, which is divided in four main parts: I) a least-squares minimization problem is formulated to find the volume of the object graspable by the robot's hand using its stereo vision; II) a recursive Bayesian filtering technique, based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose (position and orientation) of the robot's end-effector without the use of markers; III) a nonlinear constrained optimization problem is formulated to compute the desired graspable pose about the object; IV) an image-based visual servo control commands the robot's end-effector toward the desired pose. We demonstrate effectiveness and robustness of our approach with extensive experiments on the iCub humanoid robot platform, achieving real-time computation, smooth trajectories and sub-pixel precisions

Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation

Recent works have shown that large models pretrained on common visual learning tasks can provide useful representations for a wide range of specialized perception problems, as well as a variety of robotic manipulation tasks. While prior work on robotic manipulation has predominantly used frozen pretrained features, we demonstrate that in robotics this approach can fail to reach optimal performance, and that fine-tuning of the full model can lead to significantly better results. Unfortunately, fine-tuning disrupts the pretrained visual representation, and causes representational drift towards the fine-tuned task thus leading to a loss of the versatility of the original model. We introduce "lossless adaptation" to address this shortcoming of classical fine-tuning. We demonstrate that appropriate placement of our parameter efficient adapters can significantly reduce the performance gap between frozen pretrained representations and full end-to-end fine-tuning without changes to the original representation and thus preserving original capabilities of the pretrained model. We perform a comprehensive investigation across three major model architectures (ViTs, NFNets, and ResNets), supervised (ImageNet-1K classification) and self-supervised pretrained weights (CLIP, BYOL, Visual MAE) in 3 task domains and 35 individual tasks, and demonstrate that our claims are strongly validated in various settings.Comment: ICLR'23, Project page see https://sites.google.com/view/robo-adapters

An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles

Robust and performing navigation systems for Autonomous Underwater Vehicles (AUVs) play a discriminant role towards the success of complex underwater missions involving one or more AUVs. The quality of the filtering algorithm for the estimation of the AUV navigation state strongly affects the performance of the overall system. In this paper, the authors present a comparison between the Extended Kalman Filter (EKF) approach, classically used in the field of underwater robotics and an Unscented Kalman Filter (UKF). The comparison results to be significant as the two strategies of filtering are based on the same process and sensors models. The UKF-based approach, here adapted to the AUV case, demonstrates to be a good trade-off between estimation accuracy and computational load. UKF has not yet been extensively used in practical underwater applications, even if it turns out to be quite promising. The proposed results rely on the data acquired during a sea mission performed by one of the two Typhoon class vehicles involved in the NATO CommsNet13 experiment (held in September 2013). As ground truth for performance evaluation and comparison, performed offline, position measurements obtained through Ultra-Short BaseLine (USBL) fixes are used. The result analysis leads to identify both the strategies as effective for the purpose of being included in the control loop of an AUV. The UKF approach demonstrates higher performance encouraging its implementation as a more suitable navigation algorithm even if, up to now, it is still not used much in this field

Consensus CPHD filter for distributed multitarget tracking

The paper addresses distributed multitarget tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The contribution has been to develop a novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filter that provides a fully distributed, scalable and computationally efficient solution to the problem. The effectiveness of the proposed approach is demonstrated via simulation experiments on realistic scenarios

Distributed multitarget tracking with range-doppler sensors

The paper applies a recently developed Consensus Gaussian Mixture - Cardinalized Probability Hypothesis Density (CGM-CPHD) filter to distributed multitarget tracking with range and/or Doppler sensors. It is demonstrated via simulation results on realistic scenarios that the use of Doppler measurements provides a significant tracking performance improvement with respect to using low-accuracy range measurements only. On the other hand, effective distributed Doppler-only multitarget tracking is still an open issue to be investigated

Distributed Tracking with Doppler Sensors

This paper deals with the problem of tracking a moving target by means of a network of geographically dispersed nodes with Doppler sensing, communication and processing capabilities, free of any centralized coordination. The contribution is twofold. First, a suitable nonlinear observability decomposition is introduced in order to single out state coordinates that can be fully observed from a single Doppler sensor. Then, based on such a decomposition, a novel consensus filter for distributed Doppler-only tracking is developed and its performance is evaluated via computer simulations

A Novel Mitigation Scheme for JTIDS Impulsive Interference on LDACS System Based on Sensing and Symbols Retransmission

Future digital air/ground communication systems (LDACS) will operate on L-Band where the coexistence with existing legacy systems shall be guaranteed. This paper proposes a scheme to detect and mitigate the JTIDS impulsive interference on LDACS-1 system. The novel idea advised here is the transmission of two copies of the symbols received with interference that are suitably combined at the receiver after a blanking operation of the corrupted samples. In particular two alternatives are presented that differ for the retransmission policy: in the full combining scheme all the symbols are transmitted twice, in the partial combining scheme only the symbols where interference has been detected are retransmitted. These methods permit to efficiently remove the interference without affecting the useful information and exploiting profitably diversity gain against noise introduced by the soft combining approach. The numerical results provided in the paper highlight a good behaviour of the proposed methods and significant advantages in comparison with the traditional blanking method either in terms of Bit Error Rate and Throughput