We present OpenRoboCare, a multimodal dataset for robot caregiving, capturing expert occupational therapist demonstrations of Activities of Daily Living (ADLs). Caregiving tasks involve complex physical human-robot interactions, requiring precise perception under occlusions, safe physical contact, and long-horizon planning. While recent advances in robot learning from demonstrations have shown promise, there is a lack of a large-scale, diverse, and expert-driven dataset that captures real-world caregiving routines. To address this gap, we collect data from 21 occupational therapists performing 15 ADL tasks on two manikins. The dataset spans five modalities: RGB-D video, pose tracking, eye-gaze tracking, task and action annotations, and tactile sensing, providing rich multimodal insights into caregiver movement, attention, force application, and task execution strategies. We further analyze expert caregiving principles and strategies, offering insights to improve robot efficiency and task feasibility. Additionally, our evaluations demonstrate that OpenRoboCare presents challenges for state-of-the-art robot perception and human activity recognition methods, both critical for developing safe and adaptive assistive robots, highlighting the value of our contribution. See our website for additional visualizations: https://emprise.cs.cornell.edu/robo-care/.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Tesla launched its robotaxi service on Monday in Austin, with only a few cars involved but a great deal of fanfare. The discrepancy is best explained by one Wikipedia page: "List of predictions for autonomous Tesla vehicles by Elon Musk." If you are Musk, a person who is addicted to consuming every possible piece of media about yourself and who purports to hate Wikipedia for alleged "far left" bias but probably just hates it for how it portrays you specifically, this page is brutal. So far, the article has graded 21 Musk promises or predictions that he issued with a time horizon.

This paper presents a study on autonomous robot navigation, focusing on three key behaviors: Odometry, Target Tracking, and Obstacle Avoidance. Each behavior is described in detail, along with experimental setups for simulated and real-world environments. Odometry utilizes wheel encoder data for precise navigation along predefined paths, validated through experiments with a Pioneer robot. Target Tracking employs vision-based techniques for pursuing designated targets while avoiding obstacles, demonstrated on the same platform. Obstacle Avoidance utilizes ultrasonic sensors to navigate cluttered environments safely, validated in both simulated and real-world scenarios. Additionally, the paper extends the project to include an Elegoo robot car, leveraging its features for enhanced experimentation. Through advanced algorithms and experimental validations, this study provides insights into developing robust navigation systems for autonomous robots.

This paper introduces an attacking mechanism to challenge the resilience of autonomous driving systems. Specifically, we manipulate the decision-making processes of an autonomous vehicle by dynamically displaying adversarial patches on a screen mounted on another moving vehicle. These patches are optimized to deceive the object detection models into misclassifying targeted objects, e.g., traffic signs. Such manipulation has significant implications for critical multi-vehicle interactions such as intersection crossing and lane changing, which are vital for safe and efficient autonomous driving systems. Particularly, we make four major contributions. First, we introduce a novel adversarial attack approach where the patch is not co-located with its target, enabling more versatile and stealthy attacks. Moreover, our method utilizes dynamic patches displayed on a screen, allowing for adaptive changes and movement, enhancing the flexibility and performance of the attack. To do so, we design a Screen Image Transformation Network (SIT-Net), which simulates environmental effects on the displayed images, narrowing the gap between simulated and real-world scenarios. Further, we integrate a positional loss term into the adversarial training process to increase the success rate of the dynamic attack. Finally, we shift the focus from merely attacking perceptual systems to influencing the decision-making algorithms of self-driving systems. Our experiments demonstrate the first successful implementation of such dynamic adversarial attacks in real-world autonomous driving scenarios, paving the way for advancements in the field of robust and secure autonomous driving.

Conditional behavior prediction (CBP) builds up the foundation for a coherent interactive prediction and planning framework that can enable more efficient and less conservative maneuvers in interactive scenarios. In CBP task, we train a prediction model approximating the posterior distribution of target agents' future trajectories conditioned on the future trajectory of an assigned ego agent. However, we argue that CBP may provide overly confident anticipation on how the autonomous agent may influence the target agents' behavior. Consequently, it is risky for the planner to query a CBP model. Instead, we should treat the planned trajectory as an intervention and let the model learn the trajectory distribution under intervention. We refer to it as the interventional behavior prediction (IBP) task. Moreover, to properly evaluate an IBP model with offline datasets, we propose a Shapley-value-based metric to verify if the prediction model satisfies the inherent temporal independence of an interventional distribution. We show that the proposed metric can effectively identify a CBP model violating the temporal independence, which plays an important role when establishing IBP benchmarks.

In addition to hoverboards, unicycles, mopeds, and dog-pulled skateboards – as well as an occasional car or bike – San Franciscans will soon be sharing the roads with driverless robocars, zipping through traffic without the added weight of human passengers. Last October Cruise LLC received a permit to test up to five vehicles at a time within City limits without a human in the driver's seat. Cruise is the fifth company allowed to conduct such field work in California. San Franciscans have seen plenty of self-driving cars, but always with human passengers. Usually identifiable by prominent logos and strangely protruding sensors, autonomous vehicles (AV) have been approved for testing on California's roadways since 2014.

Your report on the use of robots in care homes follows a familiar and dispiriting pattern (Robots to be used in UK care homes to help reduce loneliness, 7 September). Interventions of all kinds, from art groups to yoga, have been shown to improve residents' mood and mental state, at least temporarily. Most of these interventions clearly have their own intrinsic value, but what most studies fail to account for is the grim and barren social environment that residents too commonly inhabit. Field work has shown that loneliness, isolation and a lack of human interaction is all too common within care homes. These places are generally understaffed and this is the result of chronic underfunding of the care sector.

The driverless car startup Nuro is deploying its fleet of robocars in Texas to deliver people's prescriptions. According to the company, its fleet of tiny self-driving cars will start delivering prescriptions next month to CVS customers in Houston at no extra charge. The company says it will start making deliveries with its autonomous fleet of Toyota Prius' and then switch to its smaller and more dedicated robot, the R2. For now, a safety driver will be accompanying the cars until Nuro switches to its completely autonomous R2. Eligible customers in three zip codes will be able to use the CVS website or the company's pharmacy app to order prescriptions online.

Nuro, the self-driving delivery startup, is teaming up with Domino's to launch a pilot for driverless pizza delivery in Houston, Texas, the companies announced Monday. Starting later this year, Domino's will use Nuro's driverless fleet of custom-built robot cars to deliver pizza to select Houston residents who place orders online. Nuro, which was founded by two ex-members of Google's pioneering self-driving team, has been using its fleet of R1 robot cars to deliver groceries to residents of Scottsdale, Arizona, and more recently, Houston. If the pilot with Domino's goes well, it's safe to assume Nuro will look to expand it to other markets as well. Nuro has been ramping up its activities in recent months since receiving a $1 billion investment from Japanese tech company SoftBank.

Autonomous cars have to navigate in dynamic environment which can be full of uncertainties. The uncertainties can come either from sensor limitations such as occlusions and limited sensor range, or from probabilistic prediction of other road participants, or from unknown social behavior in a new area. To safely and efficiently drive in the presence of these uncertainties, the decision-making and planning modules of autonomous cars should intelligently utilize all available information and appropriately tackle the uncertainties so that proper driving strategies can be generated. In this paper, we propose a social perception scheme which treats all road participants as distributed sensors in a sensor network. By observing the individual behaviors as well as the group behaviors, uncertainties of the three types can be updated uniformly in a belief space. The updated beliefs from the social perception are then explicitly incorporated into a probabilistic planning framework based on Model Predictive Control (MPC). The cost function of the MPC is learned via inverse reinforcement learning (IRL). Such an integrated probabilistic planning module with socially enhanced perception enables the autonomous vehicles to generate behaviors which are defensive but not overly conservative, and socially compatible. The effectiveness of the proposed framework is verified in simulation on an representative scenario with sensor occlusions.