Tootfinder

Looking for a gps logger that :
- has a few days of autonomy
- has enough memory for a few weeks of tracing at 18hours a day
Ideas, recommendations?
#osm #gps

AI robot tank. Hope it's hallucination-free.
https://www.techspot.com/news/102769-darpa-unleashes-20-foot-autonomous-robo-tank-glowing.html

HP senkt Forderung in Übernahmedebakel auf 4 Milliarden Dollar
Hewlett-Packard hat vor über 12 Jahren Autonomy übernommen und dann massiv geschönte Zahlen entdeckt. Doch HPs Schadenersatzforderung war "wesentlich" überhöht.

Tech tycoon Mike Lynch’s stunning fall
‘Britain’s Bill Gates’ could spend up to 25 years in jail if convicted of fraud in the 2011 sale of Autonomy to Hewlett-Packard
https://www.theguardian.com/business/2024/mar/16/mike…

GeoSACS: Geometric Shared Autonomy via Canal Surfaces
Shalutha Rajapakshe, Atharva Dastenavar, Michael Hagenow, Jean-Marc Odobez, Emmanuel Senft
https://arxiv.org/abs/2404.09584 <…

GeoSACS: Geometric Shared Autonomy via Canal Surfaces
We introduce GeoSACS, a geometric framework for shared autonomy (SA). In variable environments, SA methods can be used to combine robotic capabilities with real-time human input in a way that offloads the physical task from the human. To remain intuitive, it can be helpful to simplify requirements for human input (i.e., reduce the dimensionality), which create challenges for to map low-dimensional human inputs to the higher dimensional control space of robots without requiring large amounts of …

This https://arxiv.org/abs/2309.13456 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

An Optimal Control Framework for Influencing Human Driving Behavior in Mixed-Autonomy Traffic
As autonomous vehicles (AVs) become increasingly prevalent, their interaction with human drivers presents a critical challenge. Current AVs lack social awareness, causing behavior that is often awkward or unsafe. To combat this, social AVs, which are proactive rather than reactive in their behavior, have been explored in recent years. With knowledge of robot-human interaction dynamics, a social AV can influence a human driver to exhibit desired behaviors by strategically altering its own behavi…

From Agent Autonomy to Casual Collaboration: A Design Investigation on Help-Seeking Urban Robots
Xinyan Yu, Marius Hoggenmueller, Martin Tomitsch
https://arxiv.org/abs/2403.06774 …

From Agent Autonomy to Casual Collaboration: A Design Investigation on Help-Seeking Urban Robots
As intelligent agents transition from controlled to uncontrolled environments, they face challenges that sometimes exceed their operational capabilities. In many scenarios, they rely on assistance from bystanders to overcome those challenges. Using robots that get stuck in urban settings as an example, we investigate how agents can prompt bystanders into providing assistance. We conducted four focus group sessions with 17 participants that involved bodystorming, where participants assumed the r…

Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping
Shubhra Aich, Wenshan Wang, Parv Maheshwari, Matthew Sivaprakasam, Samuel Triest, Cherie Ho, Jason M. Gregory, John G. Rogers III, Sebastian Scherer
https://arxiv.org/abs/2403.11876

Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping
The limited sensing resolution of resource-constrained off-road vehicles poses significant challenges towards reliable off-road autonomy. To overcome this limitation, we propose a general framework based on fusing the future information (i.e. future fusion) for self-supervision. Recent approaches exploit this future information alongside the hand-crafted heuristics to directly supervise the targeted downstream tasks (e.g. traversability estimation). However, in this paper, we opt for a more gen…

Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping
Shubhra Aich, Wenshan Wang, Parv Maheshwari, Matthew Sivaprakasam, Samuel Triest, Cherie Ho, Jason M. Gregory, John G. Rogers III, Sebastian Scherer
https://arxiv.org/abs/2403.11876

Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping
The limited sensing resolution of resource-constrained off-road vehicles poses significant challenges towards reliable off-road autonomy. To overcome this limitation, we propose a general framework based on fusing the future information (i.e. future fusion) for self-supervision. Recent approaches exploit this future information alongside the hand-crafted heuristics to directly supervise the targeted downstream tasks (e.g. traversability estimation). However, in this paper, we opt for a more gen…

Safe Guaranteed Exploration for Non-linear Systems
Manish Prajapat, Johannes K\"ohler, Matteo Turchetta, Andreas Krause, Melanie N. Zeilinger
https://arxiv.org/abs/2402.06562

Safe Guaranteed Exploration for Non-linear Systems
Safely exploring environments with a-priori unknown constraints is a fundamental challenge that restricts the autonomy of robots. While safety is paramount, guarantees on sufficient exploration are also crucial for ensuring autonomous task completion. To address these challenges, we propose a novel safe guaranteed exploration framework using optimal control, which achieves first-of-its-kind results: guaranteed exploration for non-linear systems with finite time sample complexity bounds, while b…