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Grasp detection is a significant research direction in the field of robotics. Traditional analysis methods typically require prior knowledge of the object parameters, limiting grasp detection to structured environments and resulting in suboptimal performance. In recent years, the generative convolutional neural network (GCNN) has gained increasing attention, but they suffer from issues such as insufficient feature extraction capabilities and redundant noise. Therefore, we proposed an improved method for the GCNN, aimed at enabling fast and accurate grasp detection. First, a two-dimensional (2D) Gaussian kernel was introduced to re-encode grasp quality to address the issue of false positives in grasp rectangular metrics, emphasizing high-quality grasp poses near the central point. Additionally, to address the insufficient feature extraction capabilities of the shallow network, a receptive field module was added at the neck to enhance the network’s ability to extract distinctive features. Furthermore, the rich feature information in the decoding phase often contains redundant noise. To address this, we introduced a global-local feature fusion module to suppress noise and enhance features, enabling the model to focus more on target information. Finally, relevant evaluation experiments were conducted on public grasping datasets, including Cornell, Jacquard, and GraspNet-1 Billion, as well as in real-world robotic grasping scenarios. All results showed that the proposed method performs excellently in both prediction accuracy and inference speed and is practically feasible for robotic grasping.
Robotic manufacturing systems offer significant advantages, including increased flexibility and reduced costs. However, challenges in trajectory planning, error compensation, and system integration hinder their broader application in additive manufacturing. To address these issues, this paper proposes a generalized non-parametric trajectory planning method tailored for robotic additive manufacturing. The proposed trajectory planner incorporates chord error and speed continuity constraints and integrates the look-ahead planning with real-time interpolation in a parallel structure to ensure smooth transitions in the robot’s trajectory. Additionally, a real-time path tracking control method is introduced, combining RBF neural network-based dynamic feedforward control with visual servoing-based feedback control. This control strategy significantly improves the robot’s tracking accuracy, particularly for complex additive manufacturing paths that involve multiple short connected line segments and frequent speed variations. The effectiveness of the proposed methods is validated through experiments on a robotic additive manufacturing platform. The experimental results (line segment, circular arc segment, and continuous path tracking) show that the robot’s tracking error remains within $\pm$0.15 mm and $\pm 0.05^{\circ }$.
The chapter discusses the evolution of justice and dispute resolution in the era of LawTech (LT). Traditional taxonomies of justice are mirrored in new forms of digital dispute settlement (DDS), where the idealized Justice Hercules is compared to the prospect of robo-judges. Currently, LT primarily supports traditional courts as they transition to e-courts. Alternative dispute resolution (ADR) is evolving into online dispute resolution (ODR), with blockchain-based crowdsourcing emerging as a potential alternative to traditional justice. Hybrid models of dispute resolution are also taking shape. The chapter outlines assessment criteria for adopting LT in digital systems, focusing on ensuring that DS in the digital economy remains independent, impartial, and enforceable. Human centricity is core construct for the co-development of LT and DS. This overarching principle requires human oversight, transparency, data privacy, and fairness in both access and outcomes.
Technological disruption leads to discontent in the law, regarding the limited remedies that are available under private law. The source of the problem is a ‘private law’ model that assumes that the function of law is to correct wrongs by compensating individuals who are harmed. So, the model is based on (i) individual claimants and (ii) financial redress. If we copy this private law model into our regulatory regimes for new technologies our governance remedies will fall short. On the one hand, the use of AI can affect in a single act a large number of people. On the other hand, not all offences can be cured through awarding money damages. Therefore, it is necessary to rethink private remedies in the face of AI wrongs to make law effective. To achieve this, the mantra of individual compensation has to be overcome in favor of a social perspective should prevail including the use of non-pecuniary measures to provide effective remedies for AI wrongs.
Provided the law’s classifications are broadly drawn, technological innovation will not require the classifications to be redrawn or new categories to be introduced. This is not to say, however, that innovations will never require a rethinking of old categories or the invention of new ones. Difficult as that may be, the more difficult issue is detecting disruptions in the first place. Some truly disruptive innovations, such as computer programs, may be hidden from view for a variety of reasons. Others, touted as disruptive, such as cryptoassets, may not really be the case.
Collaborative design (co-design) is a team effort fostered through the creative involvement of all participants in co-creative collaboration (co-creation). This new approach to design as a creative social activity heightens the need to study the interpersonal aspects of creativity. Though co-creation has become widely used in recent years, few studies focus on its dynamics, which emerge from intense interactions created by the shared subjectivities of participants in an intersubjective environment. The management and enhancement of interpersonal factors can help create this shared environment by leading the process from personal to interpersonal creativity. Some of these interpersonal factors could be measured by observing the data of biosignals that are used as social cues, particularly if studied in comparison with the data of one of the partners of the social interaction, thanks to the synchrony rate between these datasets. This synchrony of biosignals related to shared behaviours can be associated with the interactive level dynamics that occur during co-creation in team of two (pairwork). This study presents the results of an experiment where biosignal synchrony results were compared to subjective feedback regarding the interactive level to understand the dynamics of the interaction. The results suggest the possibility of using the synchrony rate measured by the Damerau- Levenshtein distance (Ld) or dynamic time warping method (DTW) to approximate the dynamics of the interactive level in co-creative pairwork. This study will contribute to our understanding of the influence of the socio-cognitive process on interactions during co-creation to improve the co-creative design process.
Failures of environmental law to preserve, protect and improve the environment are caused by law’s contingency and constitutional presumptions of supremacy over the self-regulatory agency of nature. Contingency problems are intrinsic to law and, therefore, invite deployment of technologies. Constitutional presumptions can be corrected through geo-constitutional reform. The latter requires the elaboration of geo-constitutional principles bestowing authority on nature’s self-regulatory agency. It is suggested that principles of autonomy, loyalty, pre-emption, supremacy and rights have potential to serve that aim and imply proactive roles for technologies in environmental governance. Geo-constitutional reform is necessary to prevent the fatal collapse of the natural regulatory infrastructure enabling life and a future of environmental governance by design. Once environmental catastrophe has materialized, however, geo-constitutionalism loses its raison d’être.
This chapter argues that, as evidenced by EU digital law and EU border management, the EU legislature is complicit in the creation of complex socio-technical systems that undermine core features of the EU’s legal culture. In the case of digital law, while the EU continues to govern by publicly declared and debated legal rules, the legal frameworks – exemplified by the AI Act – are excessively complex and opaque. In the case of border management, the EU increasingly relies not on governance by law but on governance by various kinds of technological instruments. Such striking departures from the EU’s constitutive commitments to the rule of law, democracy and respect for human rights, are more than a cause for concern; they raise profound questions about what it now means to be a European.
This chapter challenges the conventional wisdom of how users of social media platforms such as Instagram, X, or TikTok pay for service access. It argues that rather than merely exchanging data for services, users unknowingly barter their attention, emotions, and cognitive resources – mental goods that corporations exploit through technologically managed systems like targeted advertising and habit-forming design. The chapter explores how these transactions are facilitated not by legal contracts but by code, which allows social media companies to extract value in ways that traditional legal conceptual frameworks cannot capture. It further highlights the negative externalities of these exchanges, such as cognitive impairments and mental health issues, framing them as pollution byproducts of the attention economy. By examining both the visible and hidden dimensions of this technologically mediated exchange, the chapter calls for a deeper understanding of the mechanisms that govern our interactions with digital platforms rather than rushing to propose new legal solutions.
Advanced AI (generative AI) poses challenges to the practice of law and to society as a whole. The proper governance of AI is unresolved but will likely be multifaceted (soft law such as standardisation, best practices and ethical guidelines), as well as hard law consisting of a blend of existing law and new regulations. This chapter argues that ‘lawyer’s professional codes’ of conduct (ethical guidelines) provide a governance system that can be applied to the AI industry. The increase in professionalisation warrants the treating of AI creators, developers and operators, as professionals subject to the obligations foisted on the legal profession and other learned professions. Legal ethics provides an overall conceptual structure that can guide AI development serving the purposes of disclosing potential liabilities to AI developers and building trust for the users of AI. Additionally, AI creators, developers and operators should be subject to fiduciary duty law. Fiduciary duty law as applied to these professionals would require a duty of care in designing safe AI systems, a duty of loyalty to customers, users and society not to create systems that manipulate consumers and democratic governance and a duty of good faith to create beneficial systems. This chapter advocates the use of ethical guidelines and fiduciary law not as soft law but as the basis of structuring private law in the governance of AI.
Law’s governance seemingly faces an uncertain future. In one direction, the alternative to law’s governance is a dangerous state of disorder and, potentially, existential threats to humanity. That is not the direction in which we should be going, and we do not want our escalating discontent with law’s governance to give it any assistance. Law’s governance is already held in contempt by many. In the other direction, if we pursue technological solutions to the imperfections in law’s governance, there is a risk that we diminish the importance of humans and their agency. If any community is contemplating transition to governance by technology, it needs to start its impact assessment with the question of whether the new tools are compatible with sustaining the foundational conditions themselves.
This chapter analyses the public and private governance structure of the EU AI Act (AIA) and its associated ecosystem of compliance and conformity. Firstly, the interaction of public and private governance in the making of AI law meant to concretise the rules in the AIA is analysed. Secondly, the focus shifts to the interaction of public and private governance in the Act’s enforcement through compliance, conformity and public authorities. Thirdly, it is argued that the EU legislature has neither fully developed public private governance nor the interaction between the two. As a result, there are many gaps in the involvement of civil society in compliance, conformity and enforcement of private regulations, in particular harmonized technical standards, Codes of Practice and Codes of Conduct. Moreover, the extreme complexity of the AIA’s governance structure is likely to trigger litigation between AI providers and deployers and the competent surveillance authorities, or more generally in B2B and B2C relations.
This chapter examines three reasons for discontent with law’s governance of technology. Reservations concern the exercise of legal powers, the convenience of legal regulations, and prestige. The analysis is supplemented with the impact that the pace of technological innovation has on legal systems and the distinction between internal and external problems of legal governance. The internal problems regard the efficacy, efficiency, and overall soundness of the normative acts; the external problems are related to the claims of further regulatory systems in society, such as the forces of the market, or of social customs. By following the recommendations of Leibniz in the sixth paragraph of his Discourse on Metaphysics, the overall idea is to discuss the simplest possible hypothesis to attain the richest world of phenomena. Discontent with law’s governance of technology is indeed a complex topic with manifold polymorphous ramifications.
This chapter presents an extended critique of the Quoine case in Singapore where the seven trades at issue were fully automated. The central point of the case is that one or both contracting parties decided to deploy or rely on technological assistance and that does not in itself justify a departure or a deviation from long-standing legal principles of contract law. While there is no denying that the contracting process can be optimized by means of a broad range of technologies of varying complexity and that such technologies often create unique risks, it does not follow that such technologies have a disruptive effect on contract law itself. Innovation in commercial dealings need not lead to an innovation in contract law. To the contrary, the latter has shown a surprising resilience to technological disruption, mainly due to the broad, flexible, and technology neutral formulation of its core principles.