9 research outputs found
Generalized Approximate Message-Passing Decoder for Universal Sparse Superposition Codes
Sparse superposition (SS) codes were originally proposed as a
capacity-achieving communication scheme over the additive white Gaussian noise
channel (AWGNC) [1]. Very recently, it was discovered that these codes are
universal, in the sense that they achieve capacity over any memoryless channel
under generalized approximate message-passing (GAMP) decoding [2], although
this decoder has never been stated for SS codes. In this contribution we
introduce the GAMP decoder for SS codes, we confirm empirically the
universality of this communication scheme through its study on various channels
and we provide the main analysis tools: state evolution and potential. We also
compare the performance of GAMP with the Bayes-optimal MMSE decoder. We
empirically illustrate that despite the presence of a phase transition
preventing GAMP to reach the optimal performance, spatial coupling allows to
boost the performance that eventually tends to capacity in a proper limit. We
also prove that, in contrast with the AWGNC case, SS codes for binary input
channels have a vanishing error floor in the limit of large codewords.
Moreover, the performance of Hadamard-based encoders is assessed for practical
implementations
Assistive Teaching of Motor Control Tasks to Humans
Recent works on shared autonomy and assistive-AI technologies, such as
assistive robot teleoperation, seek to model and help human users with limited
ability in a fixed task. However, these approaches often fail to account for
humans' ability to adapt and eventually learn how to execute a control task
themselves. Furthermore, in applications where it may be desirable for a human
to intervene, these methods may inhibit their ability to learn how to succeed
with full self-control. In this paper, we focus on the problem of assistive
teaching of motor control tasks such as parking a car or landing an aircraft.
Despite their ubiquitous role in humans' daily activities and occupations,
motor tasks are rarely taught in a uniform way due to their high complexity and
variance. We propose an AI-assisted teaching algorithm that leverages skill
discovery methods from reinforcement learning (RL) to (i) break down any motor
control task into teachable skills, (ii) construct novel drill sequences, and
(iii) individualize curricula to students with different capabilities. Through
an extensive mix of synthetic and user studies on two motor control tasks --
parking a car with a joystick and writing characters from the Balinese alphabet
-- we show that assisted teaching with skills improves student performance by
around 40% compared to practicing full trajectories without skills, and
practicing with individualized drills can result in up to 25% further
improvement. Our source code is available at
https://github.com/Stanford-ILIAD/teachingComment: 22 pages, 14 figures, NeurIPS 202
When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans
In order to collaborate safely and efficiently, robots need to anticipate how
their human partners will behave. Some of today's robots model humans as if
they were also robots, and assume users are always optimal. Other robots
account for human limitations, and relax this assumption so that the human is
noisily rational. Both of these models make sense when the human receives
deterministic rewards: i.e., gaining either 130 with certainty. But in
real world scenarios, rewards are rarely deterministic. Instead, we must make
choices subject to risk and uncertainty--and in these settings, humans exhibit
a cognitive bias towards suboptimal behavior. For example, when deciding
between gaining 130 only 80% of the time, people tend
to make the risk-averse choice--even though it leads to a lower expected gain!
In this paper, we adopt a well-known Risk-Aware human model from behavioral
economics called Cumulative Prospect Theory and enable robots to leverage this
model during human-robot interaction (HRI). In our user studies, we offer
supporting evidence that the Risk-Aware model more accurately predicts
suboptimal human behavior. We find that this increased modeling accuracy
results in safer and more efficient human-robot collaboration. Overall, we
extend existing rational human models so that collaborative robots can
anticipate and plan around suboptimal human behavior during HRI.Comment: ACM/IEEE International Conference on Human-Robot Interactio
Altruistic Autonomy: Beating Congestion on Shared Roads
Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion, both by increasing network throughput and by enabling a social planner to incentivize users of autonomous vehicles to take longer routes that can alleviate congestion on more direct roads. We formalize the effects of altruistic autonomy on roads shared between human drivers and autonomous vehicles. In this work, we develop a formal model of road congestion on shared roads based on the fundamental diagram of traffic. We consider a network of parallel roads and provide algorithms that compute optimal equilibria that are robust to additional unforeseen demand. We further plan for optimal routings when users have varying degrees of altruism. We find that even with arbitrarily small altruism, total latency can be unboundedly better than without altruism, and that the best selfish equilibrium can be unboundedly better than the worst selfish equilibrium. We validate our theoretical results through microscopic traffic simulations and show average latency decrease of a factor of 4 from worst-case selfish equilibrium to the optimal equilibrium when autonomous vehicles are altruistic
Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams
When humans collaborate with each other, they often make decisions by observing others and considering the consequences that their actions may have on the entire team, instead of greedily doing what is best for just themselves. We would like our AI agents to effectively collaborate in a similar way by capturing a model of their partners. In this work, we propose and analyze a decentralized Multi-Armed Bandit (MAB) problem with coupled rewards as an abstraction of more general multi-agent collaboration. We demonstrate that naive extensions of single-agent optimal MAB algorithms fail when applied for decentralized bandit teams. Instead, we propose a Partner-Aware strategy for joint sequential decision-making that extends the well-known single-agent Upper Confidence Bound algorithm. We analytically show that our proposed strategy achieves logarithmic regret, and provide extensive experiments involving human-AI and human-robot collaboration to validate our theoretical findings. Our results show that the proposed partner-aware strategy outperforms other known methods, and our human subject studies suggest humans prefer to collaborate with AI agents implementing our partner-aware strategy
Serum endocan levels, carotid intima-media thickness and microalbuminuria in patients with newly diagnosed hypertension
Background: Endocan is a particular protein of endothelial cells. The purpose of this study was to determine the relationship of serum endocan levels with carotid intima-media thickness (cIMT), inflammation, and microalbuminuria in patients with newly-diagnosed hypertension. Materials-Methods: This prospective study included 61 patients with newly-diagnosed hypertension (HT) and 30 controls. Endocan, microalbuminuria and cIMT measurements were taken from all patients. Results: The serum endocan levels, the mean cIMT and microalbuminuria levels of patients with HT were significantly higher than those of the control group (p < .0001, p = .015 and p < .001, respectively). Conclusion: We found that endocan levels were increased in our study. This increase in endocan levels shows a relation with cIMT and microalbuminuria, which are associated with endothelial dysfunction
Serum endocan levels, carotid intima-media thickness and microalbuminuria in patients with newly diagnosed hypertension
Interactive Tuning of Robot Program Parameters via Expected Divergence Maximization
Enabling diverse users to program robots for different applications is critical for robots to be widely adopted. Most of the new collaborative robot manipulators come with intuitive programming interfaces that allow novice users to compose robot programs and tune their parameters. However, parameters like motion speeds or exerted forces cannot be easily demonstrated and often require manual tuning, resulting in a tedious trial-and-error process. To address this problem, we formulate tuning of one-dimensional parameters as an Active Learning problem where the learner iteratively refines its estimate of the feasible range of parameter values, by selecting informative queries. By executing the parametrized actions, the learner gathers the user's feedback, in the form of directional answers ("higher,'' "lower,'' or "fine''), and integrates it in the estimate. We propose an Active Learning approach based on Expected Divergence Maximization for this setting and compare it against two baselines with synthetic data. We further compare the approaches on a real-robot dataset obtained from programs written with a simple Domain-Specific Language for a robot arm and manually tuned by expert users (N=8) to perform four manipulation tasks. We evaluate the effectiveness and usability of our interactive tuning approach against manual tuning with a user study where novice users (N=8) tuned parameters of a human-robot hand-over program.Peer reviewe